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Top 10 Machine Learning Research Papers of 2021

Machine learning research papers showcasing the transformation of the technology

Unbiased gradient estimation in unrolled computation graphs with persistent evolution, solving high-dimensional parabolic pdes using the tensor train format.

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Oops I took a gradient: Scalable sampling for discrete distributions

Optimal complexity in decentralized training, understanding self-supervised learning dynamics without contrastive pairs, how transferable are featured in deep neural networks, do we need hundreds of classifiers to solve real-world classification problems, knowledge vault: a web-scale approach to probabilistic knowledge fusion, scalable nearest neighbor algorithms for high dimensional data, trends in extreme learning machines.

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Blockchain booms, risks loom: the ai rescue mission in smart contract auditing, developing incident response plans for insider threats, weis wave: revolutionizing market analysis, top machine learning (ml) research papers released in 2022.

For every Machine Learning (ML) enthusiast, we bring you a curated list of the major breakthroughs in ML research in 2022.

Machine learning (ML) is gaining much traction in recent years owing to the disruption and development it brings in enhancing existing technologies. Every month, hundreds of ML papers from various organizations and universities get uploaded on the internet to share the latest breakthroughs in this domain. As the year ends, we bring you the Top 22 ML research papers of 2022 that created a huge impact in the industry. The following list does not reflect the ranking of the papers, and they have been selected on the basis of the recognitions and awards received at international conferences in machine learning.

• Bootstrapped Meta-Learning

Meta-learning is a promising field that investigates ways to enable machine learners or RL agents (which include hyperparameters) to learn how to learn in a quicker and more robust manner, and it is a crucial study area for enhancing the efficiency of AI agents.

This 2022 ML paper presents an algorithm that teaches the meta-learner how to overcome the meta-optimization challenge and myopic meta goals. The algorithm’s primary objective is meta-learning using gradients, which ensures improved performance. The research paper also examines the potential benefits due to bootstrapping. The authors highlight several interesting theoretical aspects of this algorithm, and the empirical results achieve new state-of-the-art (SOTA) on the ATARI ALE benchmark as well as increased efficiency in multitask learning.

• Competition-level code generation with AlphaCode

One of the exciting uses for deep learning and large language models is programming. The rising need for coders has sparked the race to build tools that can increase developer productivity and provide non-developers with tools to create software. However, these models still perform badly when put to the test on more challenging, unforeseen issues that need more than just converting instructions into code.

The popular ML paper of 2022 introduces AlphaCode, a code generation system that, in simulated assessments of programming contests on the Codeforces platform, averaged a rating in the top 54.3%. The paper describes the architecture, training, and testing of the deep-learning model.

• Restoring and attributing ancient texts using deep neural networks

The epigraphic evidence of the ancient Greek era — inscriptions created on durable materials such as stone and pottery —  had already been broken when it was discovered, rendering the inscribed writings incomprehensible. Machine learning can help in restoring, and identifying chronological and geographical origins of damaged inscriptions to help us better understand our past. 

This ML paper proposed a machine learning model built by DeepMind, Ithaca, for the textual restoration and geographical and chronological attribution of ancient Greek inscriptions. Ithaca was trained on a database of just under 80,000 inscriptions from the Packard Humanities Institute. It had a 62% accuracy rate compared to historians, who had a 25% accuracy rate on average. But when historians used Ithaca, they quickly achieved a 72% accuracy.

• Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Large neural networks use more resources to train hyperparameters since each time, the network must estimate which hyperparameters to utilize. This groundbreaking ML paper of 2022 suggests a novel zero-shot hyperparameter tuning paradigm for more effectively tuning massive neural networks. The research, co-authored by Microsoft Research and OpenAI, describes a novel method called µTransfer that leverages µP to zero-shot transfer hyperparameters from small models and produces nearly perfect HPs on large models without explicitly tuning them.

This method has been found to reduce the amount of trial and error necessary in the costly process of training large neural networks. By drastically lowering the need to predict which training hyperparameters to use, this approach speeds up research on massive neural networks like GPT-3 and perhaps its successors in the future.

• PaLM: Scaling Language Modeling with Pathways 

Large neural networks trained for language synthesis and recognition have demonstrated outstanding results in various tasks in recent years. This trending 2022 ML paper introduced Pathways Language Model (PaLM), a 780 billion high-quality text token, and 540 billion parameter-dense decoder-only autoregressive transformer.

Although PaLM just uses a decoder and makes changes like SwiGLU Activation, Parallel Layers, Multi-Query Attention, RoPE Embeddings, Shared Input-Output Embeddings, and No Biases and Vocabulary, it is based on a typical transformer model architecture. The paper describes the company’s latest flagship surpassing several human baselines while achieving state-of-the-art in numerous zero, one, and few-shot NLP tasks.

• Robust Speech Recognition via Large-Scale Weak Supervision

Machine learning developers have found it challenging to build speech-processing algorithms that are trained to predict a vast volume of audio transcripts on the internet. This year, OpenAI released Whisper , a new state-of-the-art (SotA) model in speech-to-text that can transcribe any audio to text and translate it into several languages. It has received 680,000 hours of training on a vast amount of voice data gathered from the internet. According to OpenAI, this model is robust to accents, background noise, and technical terminology. Additionally, it allows transcription into English from 99 different languages and translation into English from those languages.

The OpenAI ML paper mentions the author ensured that about one-third of the audio data is non-English. This helped the team outperform other supervised state-of-the-art models by maintaining a diversified dataset.

• OPT: Open Pre-trained Transformer Language Models

Large language models have demonstrated extraordinary performance f on numerous tasks (e.g., zero and few-shot learning). However, these models are difficult to duplicate without considerable funding due to their high computing costs. Even while the public can occasionally interact with these models through paid APIs, complete research access is still only available from a select group of well-funded labs. This limited access has hindered researchers’ ability to comprehend how and why these language models work, which has stalled progress on initiatives to improve their robustness and reduce ethical drawbacks like bias and toxicity.

The popular 2022 ML paper introduces Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers with 125 million to 175 billion parameters that the authors want to share freely and responsibly with interested academics. The biggest OPT model, OPT-175B (it is not included in the code repository but is accessible upon request), which is impressively proven to perform similarly to GPT-3 (which also has 175 billion parameters)  uses just 15% of GPT-3’s carbon footprint during development and training.

• A Path Towards Autonomous Machine Intelligence

Yann LeCun is a prominent and respectable researcher in the field of artificial intelligence and machine learning. In June, his much-anticipated paper “ A Path Towards Autonomous Machine Intelligence ” was published on OpenReview. LeCun offered a number of approaches and architectures in his paper that might be combined and used to create self-supervised autonomous machines. 

He presented a modular architecture for autonomous machine intelligence that combines various models to operate as distinct elements of a machine’s brain and mirror the animal brain. Due to the differentiability of all the models, they are all interconnected to power certain brain-like activities, such as identification and environmental response. It incorporates ideas like a configurable predictive world model, behavior-driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised learning. 

• LaMDA: Language Models for Dialog Applications 

Despite tremendous advances in text generation, many of the chatbots available are still rather irritating and unhelpful. This 2022 ML paper from Google describes the LaMDA — short for “Language Model for Dialogue Applications” — system, which caused the uproar this summer when a former Google engineer, Blake Lemoine, alleged that it is sentient. LaMDA is a family of large language models for dialog applications built on Google’s Transformer architecture, which is known for its efficiency and speed in language tasks such as translation. The model’s ability to be adjusted using data that has been human-annotated and the capability of consulting external sources are its most intriguing features.

The model, which has 137 billion parameters, was pre-trained using 1.56 trillon words from publicly accessible conversation data and online publications. The model is also adjusted based on the three parameters of quality, safety, and groundedness.

• Privacy for Free: How does Dataset Condensation Help Privacy?

One of the primary proposals in the award-winning ML paper is to use dataset condensation methods to retain data efficiency during model training while also providing membership privacy. The authors argue that dataset condensation, which was initially created to increase training effectiveness, is a better alternative to data generators for producing private data since it offers privacy for free. 

Though existing data generators are used to produce differentially private data for model training to minimize unintended data leakage, they result in high training costs or subpar generalization performance for the sake of data privacy. This study was published by Sony AI and received the Outstanding Paper Award at ICML 2022. 

• TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

The use of a model that converts time series into anomaly scores at each time step is essential in any system for detecting time series anomalies. Recognizing and diagnosing anomalies in multivariate time series data is critical for modern industrial applications. Unfortunately, developing a system capable of promptly and reliably identifying abnormal observations is challenging. This is attributed to a shortage of anomaly labels, excessive data volatility, and the expectations of modern applications for ultra-low inference times. 

In this study , the authors present TranAD, a deep transformer network-based anomaly detection and diagnosis model that leverages attention-based sequence encoders to quickly execute inference while being aware of the more general temporal patterns in the data. TranAD employs adversarial training to achieve stability and focus score-based self-conditioning to enable robust multi-modal feature extraction. The paper mentions extensive empirical experiments on six publicly accessible datasets show that TranAD can perform better in detection and diagnosis than state-of-the-art baseline methods with data- and time-efficient training. 

• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding 

In the last few years, generative models called “diffusion models” have been increasingly popular. This year saw these models capture the excitement of AI enthusiasts around the world. 

Going ahead of the current text to speech technology of recent times, this outstanding 2022 ML paper introduced the viral text-to-image diffusion model from Google, Imagen. This diffusion model achieves a new state-of-the-art FID score of 7.27 on the COCO dataset by combining the deep language understanding of transformer-based large language models with the photorealistic image-generating capabilities of diffusion models. A text-only frozen language model provides the text representation, and a diffusion model with two super-resolution upsampling stages, up to 1024×2014, produces the images. It employs several training approaches, including classifier-free guiding, to teach itself conditional and unconditional generation. Another important feature of Imagen is the use of dynamic thresholding, which stops the diffusion process from being saturated in specific areas of the picture, a behavior that reduces image quality, particularly when the weight placed on text conditional creation is large.

• No Language Left Behind: Scaling Human-Centered Machine Translation

This ML paper introduced the most popular Meta projects of the year 2022: NLLB-200. This paper talks about how Meta built and open-sourced this state-of-the-art AI model at FAIR, which is capable of translating 200 languages between each other. It covers every aspect of this technology: language analysis, moral issues, effect analysis, and benchmarking.

No matter what language a person speaks, accessibility via language ensures that everyone can benefit from the growth of technology. Meta claims that several languages that NLLB-200 translates, such as Kamba and Lao, are not currently supported by any translation systems in use. The tech behemoth also created a dataset called “FLORES-200” to evaluate the effectiveness of the NLLB-200 and show that accurate translations are offered. According to Meta, NLLB-200 offers an average of 44% higher-quality translations than its prior model.

• A Generalist Agent

AI pundits believe that multimodality will play a huge role in the future of Artificial General Intelligence (AGI). One of the most talked ML papers of 2022 by DeepMind introduces Gato – a generalist agent . This AGI agent is a multi-modal, multi-task, multi-embodiment network, which means that the same neural network (i.e. a single architecture with a single set of weights) can do all tasks while integrating inherently diverse types of inputs and outputs. 

DeepMind claims that the general agent can be improved with new data to perform even better on a wider range of tasks. They argue that having a general-purpose agent reduces the need for hand-crafting policy models for each region, enhances the volume and diversity of training data, and enables continuous advances in the data, computing, and model scales. A general-purpose agent can also be viewed as the first step toward artificial general intelligence, which is the ultimate goal of AGI. 

Gato demonstrates the versatility of transformer-based machine learning architectures by exhibiting their use in a variety of applications.  Unlike previous neural network systems tailored for playing games, stack blocks with a real robot arm, read words, and caption images, Gato is versatile enough to perform all of these tasks on its own, using only a single set of weights and a relatively simple architecture.

• The Forward-Forward Algorithm: Some Preliminary Investigations 

AI pioneer Geoffrey Hinton is known for writing paper on the first deep convolutional neural network and backpropagation. In his latest paper presented at NeurIPS 2022, Hinton proposed the “forward-forward algorithm,” a new learning algorithm for artificial neural networks based on our understanding of neural activations in the brain. This approach draws inspiration from Boltzmann machines (Hinton and Sejnowski, 1986) and noise contrast estimation (Gutmann and Hyvärinen, 2010). According to Hinton, forward-forward, which is still in its experimental stages, can substitute the forward and backward passes of backpropagation with two forward passes, one with positive data and the other with negative data that the network itself could generate. Further, the algorithm could simulate hardware more efficiently and provide a better explanation for the brain’s cortical learning process.

Without employing complicated regularizers, the algorithm obtained a 1.4 percent test error rate on the MNIST dataset in an empirical study, proving that it is just as effective as backpropagation.

The paper also suggests a novel “mortal computing” model that can enable the forward-forward algorithm and understand our brain’s energy-efficient processes.

• Focal Modulation Networks

In humans, the ciliary muscles alter the shape of the eye and hence the radius of the curvature lens to focus on near or distant objects. Changing the shape of the eye lens, changes the focal length of the lens. Mimicking this behavior of focal modulation in computer vision systems can be tricky.

This machine learning paper introduces FocalNet, an iterative information extraction technique that employs the premise of foveal attention to post-process Deep Neural Network (DNN) outputs by performing variable input/feature space sampling. Its attention-free design outperforms SoTA self-attention (SA) techniques in a wide range of visual benchmarks. According to the paper, focal modulation consists of three parts: According to the paper, focal modulation consists of three parts: 

a. hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from close-up to a great distance; 

b. gated aggregation to selectively gather contexts for each query token based on its content; and  

c. element-wise modulation or affine modification to inject the gathered context into the query.

• Learning inverse folding from millions of predicted structures

The field of structural biology is being fundamentally changed by cutting-edge technologies in machine learning, protein structure prediction, and innovative ultrafast structural aligners. Time and money are no longer obstacles to obtaining precise protein models and extensively annotating their functionalities. However, determining a protein sequence from its backbone atom coordinates remained a challenge for scientists. To date, machine learning methods to this challenge have been constrained by the amount of empirically determined protein structures available.

In this ICML Outstanding Paper (Runner Up) , authors explain tackling this problem by increasing training data by almost three orders of magnitude by using AlphaFold2 to predict structures for 12 million protein sequences. With the use of this additional data, a sequence-to-sequence transformer with invariant geometric input processing layers is able to recover native sequence on structurally held-out backbones in 51% of cases while recovering buried residues in 72% of cases. This is an improvement of over 10% over previous techniques. In addition to designing protein complexes, partly masked structures, binding interfaces, and numerous states, the concept generalises to a range of other more difficult tasks.

• MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

Within the AI research community, using video games as a training medium for AI has gained popularity. These autonomous agents have had great success in Atari games, Starcraft, Dota, and Go. Although these developments have gained popularity in the field of artificial intelligence research, the agents do not generalize beyond a narrow range of activities, in contrast to humans, who continually learn from open-ended tasks.

This thought-provoking 2022 ML paper suggests MineDojo, a unique framework for embodied agent research based on the well-known game Minecraft. In addition to building an internet-scale information base with Minecraft videos, tutorials, wiki pages, and forum discussions, Minecraft provides a simulation suite with tens of thousands of open-ended activities. Using MineDojo data, the author proposes a unique agent learning methodology that employs massive pre-trained video-language models as a learnt reward function. Without requiring a dense shaping reward that has been explicitly created, MinoDojo autonomous agent can perform a wide range of open-ended tasks that are stated in free-form language.

• Is Out-of-Distribution Detection Learnable?

Machine learning (supervised ML) models are frequently trained using the closed-world assumption, which assumes that the distribution of the testing data will resemble that of the training data. This assumption doesn’t hold true when used in real-world activities, which causes a considerable decline in their performance. While this performance loss is acceptable for applications like product recommendations, developing an out-of-distribution (OOD) identification algorithm is crucial to preventing ML systems from making inaccurate predictions in situations where data distribution in real-world activities typically drifts over time (self-driving cars).

In this paper , authors explore the probably approximately correct (PAC) learning theory of OOD detection, which is proposed by researchers as an open problem, to study the applicability of OOD detection. They first focus on identifying a prerequisite for OOD detection’s learnability. Following that, they attempt to show a number of impossibility theorems regarding the learnability of OOD detection in a handful yet different scenarios.

• Gradient Descent: The Ultimate Optimizer 

Gradient descent is a popular optimization approach for training machine learning models and neural networks. The ultimate aim of any machine learning (neural network) method is to optimize parameters, but selecting the ideal step size for an optimizer is difficult since it entails lengthy and error-prone manual work. Many strategies exist for automated hyperparameter optimization; however, they often incorporate additional hyperparameters to govern the hyperparameter optimization process. In this study , MIT CSAIL and Meta researchers offer a unique approach that allows gradient descent optimizers like SGD and Adam to tweak their hyperparameters automatically.

They propose learning the hyperparameters by self-using gradient descent, as well as learning the hyper-hyperparameters via gradient descent, and so on indefinitely. This paper describes an efficient approach for allowing gradient descent optimizers to autonomously adjust their own hyperparameters, which may be layered recursively to many levels. As these gradient-based optimizer towers expand in size, they become substantially less sensitive to the selection of top-level hyperparameters, reducing the load on the user to search for optimal values.

• ProcTHOR: Large-Scale Embodied AI Using Procedural Generation 

Embodied AI is a developing study field that has been influenced by recent advancements in artificial intelligence, machine learning, and computer vision. This method of computer learning makes an effort to translate this connection to artificial systems. The paper proposes ProcTHOR, a framework for procedural generation of Embodied AI environments. ProcTHOR allows researchers to sample arbitrarily huge datasets of diverse, interactive, customisable, and performant virtual environments in order to train and assess embodied agents across navigation, interaction, and manipulation tasks. 

According to the authors, models trained on ProcTHOR using only RGB images and without any explicit mapping or human task supervision achieve cutting-edge results in 6 embodied AI benchmarks for navigation, rearrangement, and arm manipulation, including the ongoing Habitat2022, AI2-THOR Rearrangement2022, and RoboTHOR challenges. The paper received the Outstanding Paper award at NeurIPS 2022.

• A Commonsense Knowledge Enhanced Network with Retrospective Loss for Emotion Recognition in Spoken Dialog

Emotion Recognition in Spoken Dialog (ERSD) has recently attracted a lot of attention due to the growth of open conversational data. This is due to the fact that excellent speech recognition algorithms have emerged as a result of the integration of emotional states in intelligent spoken human-computer interactions. Additionally, it has been demonstrated that recognizing emotions makes it possible to track the development of human-computer interactions, allowing for dynamic change of conversational strategies and impacting the result (e.g., customer feedback). But the volume of the current ERSD datasets restricts the model’s development. 

This ML paper proposes a Commonsense Knowledge Enhanced Network (CKE-Net) with a retrospective loss to carry out dialog modeling, external knowledge integration, and historical state retrospect hierarchically. 

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• Last updated November 18, 2021
• In AI Origins & Evolution

Top Machine Learning Research Papers Released In 2021

• Published on November 18, 2021
• by Dr. Nivash Jeevanandam

Advances in machine learning and deep learning research are reshaping our technology. Machine learning and deep learning have accomplished various astounding feats this year in 2021, and key research articles have resulted in technical advances used by billions of people. The research in this sector is advancing at a breakneck pace and assisting you to keep up. Here is a collection of the most important recent scientific study papers.

Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training

The authors of this work examined why ACGAN training becomes unstable as the number of classes in the dataset grows. The researchers revealed that the unstable training occurs due to a gradient explosion problem caused by the unboundedness of the input feature vectors and the classifier’s poor classification capabilities during the early training stage. The researchers presented the Data-to-Data Cross-Entropy loss (D2D-CE) and the Rebooted Auxiliary Classifier Generative Adversarial Network to alleviate the instability and reinforce ACGAN (ReACGAN). Additionally, extensive tests of ReACGAN demonstrate that it is resistant to hyperparameter selection and is compatible with a variety of architectures and differentiable augmentations.

This article is ranked #1 on CIFAR-10 for Conditional Image Generation.

For the research paper, read here .

For code, see here .

Dense Unsupervised Learning for Video Segmentation

The authors presented a straightforward and computationally fast unsupervised strategy for learning dense spacetime representations from unlabeled films in this study. The approach demonstrates rapid convergence of training and a high degree of data efficiency. Furthermore, the researchers obtain VOS accuracy superior to previous results despite employing a fraction of the previously necessary training data. The researchers acknowledge that the research findings may be utilised maliciously, such as for unlawful surveillance, and that they are excited to investigate how this skill might be used to better learn a broader spectrum of invariances by exploiting larger temporal windows in movies with complex (ego-)motion, which is more prone to disocclusions.

This study is ranked #1 on DAVIS 2017 for Unsupervised Video Object Segmentation (val).

Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases

The authors offer an atlas-based technique for producing unsupervised temporally consistent surface reconstructions by requiring a point on the canonical shape representation to translate to metrically consistent 3D locations on the reconstructed surfaces. Finally, the researchers envisage a plethora of potential applications for the method. For example, by substituting an image-based loss for the Chamfer distance, one may apply the method to RGB video sequences, which the researchers feel will spur development in video-based 3D reconstruction.

This article is ranked #1 on ANIM in the category of Surface Reconstruction. 

EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow

The researchers propose a revolutionary interactive architecture called EdgeFlow that uses user interaction data without resorting to post-processing or iterative optimisation. The suggested technique achieves state-of-the-art performance on common benchmarks due to its coarse-to-fine network design. Additionally, the researchers create an effective interactive segmentation tool that enables the user to improve the segmentation result through flexible options incrementally.

This paper is ranked #1 on Interactive Segmentation on PASCAL VOC

Learning Transferable Visual Models From Natural Language Supervision

The authors of this work examined whether it is possible to transfer the success of task-agnostic web-scale pre-training in natural language processing to another domain. The findings indicate that adopting this formula resulted in the emergence of similar behaviours in the field of computer vision, and the authors examine the social ramifications of this line of research. CLIP models learn to accomplish a range of tasks during pre-training to optimise their training objective. Using natural language prompting, CLIP can then use this task learning to enable zero-shot transfer to many existing datasets. When applied at a large scale, this technique can compete with task-specific supervised models, while there is still much space for improvement.

This research is ranked #1 on Zero-Shot Transfer Image Classification on SUN

CoAtNet: Marrying Convolution and Attention for All Data Sizes

The researchers in this article conduct a thorough examination of the features of convolutions and transformers, resulting in a principled approach for combining them into a new family of models dubbed CoAtNet. Extensive experiments demonstrate that CoAtNet combines the advantages of ConvNets and Transformers, achieving state-of-the-art performance across a range of data sizes and compute budgets. Take note that this article is currently concentrating on ImageNet classification for model construction. However, the researchers believe their approach is relevant to a broader range of applications, such as object detection and semantic segmentation.

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Showing papers in "Machine Learning in 2023"

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Exploring 250+ Machine Learning Research Topics

In recent years, machine learning has become super popular and grown very quickly. This happened because technology got better, and there’s a lot more data available. Because of this, we’ve seen lots of new and amazing things happen in different areas. Machine learning research is what makes all these cool things possible. In this blog, we’ll talk about machine learning research topics, why they’re important, how you can pick one, what areas are popular to study, what’s new and exciting, the tough problems, and where you can find help if you want to be a researcher.

Why Does Machine Learning Research Matter?

Table of Contents

Machine learning research is at the heart of the AI revolution. It underpins the development of intelligent systems capable of making predictions, automating tasks, and improving decision-making across industries. The importance of this research can be summarized as follows:

Advancements in Technology

The growth of machine learning research has led to the development of powerful algorithms, tools, and frameworks. Numerous industries, including healthcare, banking, autonomous cars, and natural language processing, have found use for these technology.

As researchers continue to push the boundaries of what’s possible, we can expect even more transformative technologies to emerge.

Real-world Applications

Machine learning research has brought about tangible changes in our daily lives. Voice assistants like Siri and Alexa, recommendation systems on streaming platforms, and personalized healthcare diagnostics are just a few examples of how this research impacts our world. 

By working on new research topics, scientists can further refine these applications and create new ones.

Economic and Industrial Impacts

The economic implications of machine learning research are substantial. Companies that harness the power of machine learning gain a competitive edge in the market. 

This creates a demand for skilled machine learning researchers, driving job opportunities and contributing to economic growth.

How to Choose the Machine Learning Research Topics?

Selecting the right machine learning research topics is crucial for your success as a machine learning researcher. Here’s a guide to help you make an informed decision:

• Understanding Your Interests

Start by considering your personal interests. Machine learning is a broad field with applications in virtually every sector. By choosing a topic that aligns with your passions, you’ll stay motivated and engaged throughout your research journey.

• Reviewing Current Trends

Stay updated on the latest trends in machine learning. Attend conferences, read research papers, and engage with the community to identify emerging research topics. Current trends often lead to exciting breakthroughs.

• Identifying Gaps in Existing Research

Sometimes, the most promising research topics involve addressing gaps in existing knowledge. These gaps may become evident through your own experiences, discussions with peers, or in the course of your studies.

• Collaborating with Experts

Collaboration is key in research. Working with experts in the field can help you refine your research topic and gain valuable insights. Seek mentors and collaborators who can guide you.

250+ Machine Learning Research Topics: Category-wise

Supervised learning.

• Explainable AI for Decision Support
• Few-shot Learning Methods
• Time Series Forecasting with Deep Learning
• Handling Imbalanced Datasets in Classification
• Regression Techniques for Non-linear Data
• Transfer Learning in Supervised Settings
• Multi-label Classification Strategies
• Semi-Supervised Learning Approaches
• Novel Feature Selection Methods
• Anomaly Detection in Supervised Scenarios
• Federated Learning for Distributed Supervised Models
• Ensemble Learning for Improved Accuracy
• Automated Hyperparameter Tuning
• Ethical Implications in Supervised Models
• Interpretability of Deep Neural Networks.

Unsupervised Learning

• Unsupervised Clustering of High-dimensional Data
• Semi-Supervised Clustering Approaches
• Density Estimation in Unsupervised Learning
• Anomaly Detection in Unsupervised Settings
• Transfer Learning for Unsupervised Tasks
• Representation Learning in Unsupervised Learning
• Outlier Detection Techniques
• Generative Models for Data Synthesis
• Manifold Learning in High-dimensional Spaces
• Unsupervised Feature Selection
• Privacy-Preserving Unsupervised Learning
• Community Detection in Complex Networks
• Clustering Interpretability and Visualization
• Unsupervised Learning for Image Segmentation
• Autoencoders for Dimensionality Reduction.

Reinforcement Learning

• Deep Reinforcement Learning in Real-world Applications
• Safe Reinforcement Learning for Autonomous Systems
• Transfer Learning in Reinforcement Learning
• Imitation Learning and Apprenticeship Learning
• Multi-agent Reinforcement Learning
• Explainable Reinforcement Learning Policies
• Hierarchical Reinforcement Learning
• Model-based Reinforcement Learning
• Curriculum Learning in Reinforcement Learning
• Reinforcement Learning in Robotics
• Exploration vs. Exploitation Strategies
• Reward Function Design and Ethical Considerations
• Reinforcement Learning in Healthcare
• Continuous Action Spaces in RL
• Reinforcement Learning for Resource Management.

Natural Language Processing (NLP)

• Multilingual and Cross-lingual NLP
• Contextualized Word Embeddings
• Bias Detection and Mitigation in NLP
• Named Entity Recognition for Low-resource Languages
• Sentiment Analysis in Social Media Text
• Dialogue Systems for Improved Customer Service
• Text Summarization for News Articles
• Low-resource Machine Translation
• Explainable NLP Models
• Coreference Resolution in NLP
• Question Answering in Specific Domains
• Detecting Fake News and Misinformation
• NLP for Healthcare: Clinical Document Understanding
• Emotion Analysis in Text
• Text Generation with Controlled Attributes.

Computer Vision

• Video Action Recognition and Event Detection
• Object Detection in Challenging Conditions (e.g., low light)
• Explainable Computer Vision Models
• Image Captioning for Accessibility
• Large-scale Image Retrieval
• Domain Adaptation in Computer Vision
• Fine-grained Image Classification
• Facial Expression Recognition
• Visual Question Answering
• Self-supervised Learning for Visual Representations
• Weakly Supervised Object Localization
• Human Pose Estimation in 3D
• Scene Understanding in Autonomous Vehicles
• Image Super-resolution
• Gaze Estimation for Human-Computer Interaction.

Deep Learning

• Neural Architecture Search for Efficient Models
• Self-attention Mechanisms and Transformers
• Interpretability in Deep Learning Models
• Robustness of Deep Neural Networks
• Generative Adversarial Networks (GANs) for Data Augmentation
• Neural Style Transfer in Art and Design
• Adversarial Attacks and Defenses
• Neural Networks for Audio and Speech Processing
• Explainable AI for Healthcare Diagnosis
• Automated Machine Learning (AutoML)
• Reinforcement Learning with Deep Neural Networks
• Model Compression and Quantization
• Lifelong Learning with Deep Learning Models
• Multimodal Learning with Vision and Language
• Federated Learning for Privacy-preserving Deep Learning.

Explainable AI

• Visualizing Model Decision Boundaries
• Saliency Maps and Feature Attribution
• Rule-based Explanations for Black-box Models
• Contrastive Explanations for Model Interpretability
• Counterfactual Explanations and What-if Analysis
• Human-centered AI for Explainable Healthcare
• Ethics and Fairness in Explainable AI
• Explanation Generation for Natural Language Processing
• Explainable AI in Financial Risk Assessment
• User-friendly Interfaces for Model Interpretability
• Scalability and Efficiency in Explainable Models
• Hybrid Models for Combined Accuracy and Explainability
• Post-hoc vs. Intrinsic Explanations
• Evaluation Metrics for Explanation Quality
• Explainable AI for Autonomous Vehicles.

Transfer Learning

• Zero-shot Learning and Few-shot Learning
• Cross-domain Transfer Learning
• Domain Adaptation for Improved Generalization
• Multilingual Transfer Learning in NLP
• Pretraining and Fine-tuning Techniques
• Lifelong Learning and Continual Learning
• Domain-specific Transfer Learning Applications
• Model Distillation for Knowledge Transfer
• Contrastive Learning for Transfer Learning
• Self-training and Pseudo-labeling
• Dynamic Adaption of Pretrained Models
• Privacy-Preserving Transfer Learning
• Unsupervised Domain Adaptation
• Negative Transfer Avoidance in Transfer Learning.

Federated Learning

• Secure Aggregation in Federated Learning
• Communication-efficient Federated Learning
• Privacy-preserving Techniques in Federated Learning
• Federated Transfer Learning
• Heterogeneous Federated Learning
• Real-world Applications of Federated Learning
• Federated Learning for Edge Devices
• Federated Learning for Healthcare Data
• Differential Privacy in Federated Learning
• Byzantine-robust Federated Learning
• Federated Learning with Non-IID Data
• Model Selection in Federated Learning
• Scalable Federated Learning for Large Datasets
• Client Selection and Sampling Strategies
• Global Model Update Synchronization in Federated Learning.

Quantum Machine Learning

• Quantum Neural Networks and Quantum Circuit Learning
• Quantum-enhanced Optimization for Machine Learning
• Quantum Data Compression and Quantum Principal Component Analysis
• Quantum Kernels and Quantum Feature Maps
• Quantum Variational Autoencoders
• Quantum Transfer Learning
• Quantum-inspired Classical Algorithms for ML
• Hybrid Quantum-Classical Models
• Quantum Machine Learning on Near-term Quantum Devices
• Quantum-inspired Reinforcement Learning
• Quantum Computing for Quantum Chemistry and Drug Discovery
• Quantum Machine Learning for Finance
• Quantum Data Structures and Quantum Databases
• Quantum-enhanced Cryptography in Machine Learning
• Quantum Generative Models and Quantum GANs.

Ethical AI and Bias Mitigation

• Fairness-aware Machine Learning Algorithms
• Bias Detection and Mitigation in Real-world Data
• Explainable AI for Ethical Decision Support
• Algorithmic Accountability and Transparency
• Privacy-preserving AI and Data Governance
• Ethical Considerations in AI for Healthcare
• Fairness in Recommender Systems
• Bias and Fairness in NLP Models
• Auditing AI Systems for Bias
• Societal Implications of AI in Criminal Justice
• Ethical AI Education and Training
• Bias Mitigation in Autonomous Vehicles
• Fair AI in Financial and Hiring Decisions
• Case Studies in Ethical AI Failures
• Legal and Policy Frameworks for Ethical AI.

Meta-Learning and AutoML

• Neural Architecture Search (NAS) for Efficient Models
• Transfer Learning in NAS
• Reinforcement Learning for NAS
• Multi-objective NAS
• Automated Data Augmentation
• Neural Architecture Optimization for Edge Devices
• Bayesian Optimization for AutoML
• Model Compression and Quantization in AutoML
• AutoML for Federated Learning
• AutoML in Healthcare Diagnostics
• Explainable AutoML
• Cost-sensitive Learning in AutoML
• AutoML for Small Data
• Human-in-the-Loop AutoML.

AI for Healthcare and Medicine

• Disease Prediction and Early Diagnosis
• Medical Image Analysis with Deep Learning
• Drug Discovery and Molecular Modeling
• Electronic Health Record Analysis
• Predictive Analytics in Healthcare
• Personalized Treatment Planning
• Healthcare Fraud Detection
• Telemedicine and Remote Patient Monitoring
• AI in Radiology and Pathology
• AI in Drug Repurposing
• AI for Medical Robotics and Surgery
• Genomic Data Analysis
• AI-powered Mental Health Assessment
• Explainable AI in Healthcare Decision Support
• AI in Epidemiology and Outbreak Prediction.

AI in Finance and Investment

• Algorithmic Trading and High-frequency Trading
• Credit Scoring and Risk Assessment
• Fraud Detection and Anti-money Laundering
• Portfolio Optimization with AI
• Financial Market Prediction
• Sentiment Analysis in Financial News
• Explainable AI in Financial Decision-making
• Algorithmic Pricing and Dynamic Pricing Strategies
• AI in Cryptocurrency and Blockchain
• Customer Behavior Analysis in Banking
• Explainable AI in Credit Decisioning
• AI in Regulatory Compliance
• Ethical AI in Financial Services
• AI for Real Estate Investment
• Automated Financial Reporting.

AI in Climate Change and Sustainability

• Climate Modeling and Prediction
• Renewable Energy Forecasting
• Smart Grid Optimization
• Energy Consumption Forecasting
• Carbon Emission Reduction with AI
• Ecosystem Monitoring and Preservation
• Precision Agriculture with AI
• AI for Wildlife Conservation
• Natural Disaster Prediction and Management
• Water Resource Management with AI
• Sustainable Transportation and Urban Planning
• Climate Change Mitigation Strategies with AI
• Environmental Impact Assessment with Machine Learning
• Eco-friendly Supply Chain Optimization
• Ethical AI in Climate-related Decision Support.

Data Privacy and Security

• Differential Privacy Mechanisms
• Federated Learning for Privacy-preserving AI
• Secure Multi-Party Computation
• Privacy-enhancing Technologies in Machine Learning
• Homomorphic Encryption for Machine Learning
• Ethical Considerations in Data Privacy
• Privacy-preserving AI in Healthcare
• AI for Secure Authentication and Access Control
• Blockchain and AI for Data Security
• Explainable Privacy in Machine Learning
• Privacy-preserving AI in Government and Public Services
• Privacy-compliant AI for IoT and Edge Devices
• Secure AI Models Sharing and Deployment
• Privacy-preserving AI in Financial Transactions
• AI in the Legal Frameworks of Data Privacy.

Global Collaboration in Research

• International Research Partnerships and Collaboration Models
• Multilingual and Cross-cultural AI Research
• Addressing Global Healthcare Challenges with AI
• Ethical Considerations in International AI Collaborations
• Interdisciplinary AI Research in Global Challenges
• AI Ethics and Human Rights in Global Research
• Data Sharing and Data Access in Global AI Research
• Cross-border Research Regulations and Compliance
• AI Innovation Hubs and International Research Centers
• AI Education and Training for Global Communities
• Humanitarian AI and AI for Sustainable Development Goals
• AI for Cultural Preservation and Heritage Protection
• Collaboration in AI-related Global Crises
• AI in Cross-cultural Communication and Understanding
• Global AI for Environmental Sustainability and Conservation.

Emerging Trends and Hot Topics in Machine Learning Research

The landscape of machine learning research topics is constantly evolving. Here are some of the emerging trends and hot topics that are shaping the field:

As AI systems become more prevalent, addressing ethical concerns and mitigating bias in algorithms are critical research areas.

Interpretable and Explainable Models

Understanding why machine learning models make specific decisions is crucial for their adoption in sensitive areas, such as healthcare and finance.

Meta-learning algorithms are designed to enable machines to learn how to learn, while AutoML aims to automate the machine learning process itself.

Machine learning is revolutionizing the healthcare sector, from diagnostic tools to drug discovery and patient care.

Algorithmic trading, risk assessment, and fraud detection are just a few applications of AI in finance, creating a wealth of research opportunities.

Machine learning research is crucial in analyzing and mitigating the impacts of climate change and promoting sustainable practices.

Challenges and Future Directions

While machine learning research has made tremendous strides, it also faces several challenges:

• Data Privacy and Security: As machine learning models require vast amounts of data, protecting individual privacy and data security are paramount concerns.
• Scalability and Efficiency: Developing efficient algorithms that can handle increasingly large datasets and complex computations remains a challenge.
• Ensuring Fairness and Transparency: Addressing bias in machine learning models and making their decisions transparent is essential for equitable AI systems.
• Quantum Computing and Machine Learning: The integration of quantum computing and machine learning has the potential to revolutionize the field, but it also presents unique challenges.
• Global Collaboration in Research: Machine learning research benefits from collaboration on a global scale. Ensuring that researchers from diverse backgrounds work together is vital for progress.

Resources for Machine Learning Researchers

If you’re looking to embark on a journey in machine learning research topics, there are various resources at your disposal:

• Journals and Conferences

Journals such as the “Journal of Machine Learning Research” and conferences like NeurIPS and ICML provide a platform for publishing and discussing research findings.

• Online Communities and Forums

Platforms like Stack Overflow, GitHub, and dedicated forums for machine learning provide spaces for collaboration and problem-solving.

• Datasets and Tools

Open-source datasets and tools like TensorFlow and PyTorch simplify the research process by providing access to data and pre-built models.

• Research Grants and Funding Opportunities

Many organizations and government agencies offer research grants and funding for machine learning projects. Seek out these opportunities to support your research.

Machine learning research is like a superhero in the world of technology. To be a part of this exciting journey, it’s important to choose the right machine learning research topics and keep up with the latest trends.

Machine learning research makes our lives better. It powers things like smart assistants and life-saving medical tools. It’s like the force driving the future of technology and society.

But, there are challenges too. We need to work together and be ethical in our research. Everyone should benefit from this technology. The future of machine learning research is incredibly bright. If you want to be a part of it, get ready for an exciting adventure. You can help create new solutions and make a big impact on the world.

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• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

• Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
• Supinya Piampongsant 1 , 2 , 3   na1 ,
• Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
• Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
• Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
• Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
• Florian A. Theßeling 1 , 2 , 3 ,
• Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
• Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
• Philippe Malcorps 6 ,
• Luk Daenen 6 ,
• Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
• Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

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• Chemical engineering
• Gas chromatography
• Machine learning
• Metabolomics
• Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

Authors and Affiliations

VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

Philippe Malcorps & Luk Daenen

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Contributions

S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

Corresponding author

Correspondence to Kevin J. Verstrepen .

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Competing interests.

K.J.V. is affiliated with bar.on. The other authors declare no competing interests.

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Nature Communications thanks Florian Bauer, Andrew John Macintosh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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Received : 30 October 2023

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DOI : https://doi.org/10.1038/s41467-024-46346-0

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CS&E Colloquium: Co-Designing Algorithms and Hardware for Efficient Machine Learning (ML): Advancing the Democratization of ML

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker,  Caiwen Ding ( University of Connecticut ), will be giving a talk titled, "Co-Designing Algorithms and Hardware for Efficient Machine Learning (ML): Advancing the Democratization of ML". 

The rapid deployment of ML has witnessed various challenges such as prolonged computation and high memory footprint on systems. In this talk, we will present several ML acceleration frameworks through algorithm-hardware co-design on various computing platforms. The first part presents a fine-grained crossbar-based ML accelerator. Instead of attempting to map the trained positive/negative weights afterwards, our key principle is to proactively ensure that all weights in the same column of a crossbar have the same sign, to reduce area. We divide the crossbar into sub-arrays, providing a unique opportunity for input zero-bit skipping. Next, we focus on co-designing Transformer architecture, and introduce on-the-fly attention and attention-aware pruning to significantly reduce runtime latency. Then, we will focus on co-design graph neural network training. To explore training sparsity and assist explainable ML, we propose a hardware friendly MaxK nonlinearity, and tailor a GPU kernel. Our methods outperform the state-of-the-arts on different tasks. Finally, we will discuss today's challenges related to secure edge AI and large language models (LLMs)-aided agile hardware design, and outline our research plans aimed at addressing these issues.

Caiwen Ding is an assistant professor in the School of Computing at the University of Connecticut (UConn). He received his Ph.D. degree from Northeastern University, Boston, in 2019, supervised by Prof. Yanzhi Wang. His research interests mainly include efficient embedded and high-performance systems for machine learning, machine learning for hardware design, and efficient privacy-preserving machine learning. His work has been published in high-impact venues (e.g., DAC, ICCAD, ASPLOS, ISCA, MICRO, HPCA, SC, FPGA, Oakland, NeurIPS, ICCV, IJCAI, AAAI, ACL, EMNLP). He is a recipient of the 2024 NSF CAREER Award, Amazon Research Award, and CISCO Research Award. He received the best paper nomination at 2018 DATE and 2021 DATE, the best paper award at the DL-Hardware Co-Design for AI Acceleration (DCAA) workshop at 2023 AAAI, outstanding student paper award at 2023 HPEC, publicity paper at 2022 DAC, and the 2021 Excellence in Teaching Award from UConn Provost. His team won first place in accuracy and fourth place overall at the 2022 TinyML Design Contest at ICCAD. He was ranked among Stanford’s World’s Top 2% Scientists in 2023. His research has been mainly funded by NSF, DOE, DOT, USDA, SRC, and multiple industrial sponsors.

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