chchenhui/mlrbench-tasks huggingface dataset:
# Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization

## About

The Workshop on Advancing Neural Network Training (WANT): Computational Efficiency, Scalability, and Resource Optimization will give all researchers the tools necessary to train neural networks at scale. It will provide an interactive platform for researchers and practitioners to delve into the latest advancements in neural network training. Our workshop focuses on practically addressing challenges to enhance computational efficiency, scalability, and resource optimization.

The unprecedented availability of data, computation and algorithms have enabled a new AI revolution, as seen in Transformers and LLMs, diffusion models, etc, resulting in revolutionary applications such as ChatGPT, generative AI and AI for science. However, all of these applications have in common an always-growing scale, which makes training models more difficult. This can be a bottleneck for the advancement of science, both at industry scale and for smaller research teams that may not have access to the same training infrastructure. By optimizing the training process, we can accelerate innovation, drive impactful applications in various domains and enable progress in applications such as AI for good and for science.

## Topics

We welcome submissions on the following topics, but not limited to:

- Training for large scale models
- Efficient training for different applications (NLP/CV/Climate/Medicine/Finance/etc.)
- Model/tensor/data and other types of parallelisms
- Pipelining
- Communication optimization
- Re-materialization (activation checkpointing)
- Offloading
- Efficient computations: tensorized layers, low-precision computations, etc.
- Energy-efficient training
- Efficient data loading and preprocessing
- Network-aware resource allocation
- Architecture-aware resource allocation
- Scheduling for AI
# New Frontiers of AI for Drug Discovery and Development

Drug discovery and development is costly, time-consuming, and highly uncertain on the outcomes. Since its emergence, AI has been envisioned to nearly every phase of drug discovery and development to accelerate time-to-market of effective medicines and to improve the quality of life while minimizing the risk of adverse reactions for patients. In this workshop, we aim to foster discussion about the challenges, discoveries, and opportunities of AI for drug discovery and development.

# Topics

We welcome submissions of original studies addressing topics relevant to AI for drug discovery and development, including but not limit to:

- Genomic representation learning
- Molecular representation learning
- Target identification
- Drug repurposing
- Molecule optimization
- Binding and affinity prediction
- Pocket-based drug design
- Structure-based drug design
- Antibody design
- Drug safety prediction
- Clinical outcomes prediction
- Precision drug dosage
- Drug characterization (e.g., solubility, stability, particle size, ...)
- Clinical trial design and optimization
- New drug discovery and development datasets and benchmarks
- Regulations on drug discovery and development
# AI for Science Workshop

## About
For centuries, the method of discovery—the fundamental practice of science that scientists use to explain the natural world systematically and logically—has remained largely the same. Artificial intelligence (AI) and machine learning (ML) hold tremendous promise in having an impact on the way scientific discovery is performed today at the fundamental level. However, to realize this promise, we need to identify priorities and outstanding open questions for the cutting edge of AI going forward. We are particularly interested in the following topics:
- Solving grand challenges in structural biology
- Scaling dynamical system modeling to millions of particles
- Visualizing the unimaginable black hole
- Incorporating physical insights to AI methods
- Accelerating drug discovery pipeline

## Topics

Example topics include (but not limited to):
- Learning from acoustics
- Learning physical dynamics from data
- Speeding up physical simulators, samplers and solvers
- Molecular modeling and de novo generation
- Modeling biological systems, genomics, protein, RNA
- Accelerating cosmological simulations
- Improving crop yields through precision agriculture
- Optimizing aerospace product design and development
- Benchmarking related or new tasks (i.e., datasets, sota models, etc.)
- Building tools/infrastructures/platforms for scientific discovery
- Study of science of science/scientific methods
# CompSust-2023: 2023 NeurIPS Workshop on Computational Sustainability: Pitfalls and Promises from Theory to Deployment

Computational sustainability (CompSust) is an interdisciplinary research area that uses computational methods to help address the 17 United Nations Sustainable Development Goals (UN SDGs), including but not limited to hunger and poverty reduction, infrastructure development, and environmental conservation. Computational sustainability is a two-way street: sustainability domains benefit from computational tools and methods and computational research areas benefit from the unique challenges that arise in attempting to address sustainability problems, including noisy and biased data, complex multi-agent systems, and multi-objective problems. Previous computational sustainability problems have led to new approaches in computer vision, reinforcement learning, multi-agent systems, and decision-focused learning. While computational sustainability problems span many domains, they share common challenges.

The Computational Sustainability Workshop @ NeurIPS 2023 (CompSust 2023) focuses on computational methods for balancing environmental, economic, and societal needs for a sustainable future. The theme of this workshop is “Promises and Pitfalls from Theory to Deployment.” This workshop will bring the community together to focus on two topics:
- The path from theory to deployment: While a goal of computational sustainability is to achieve broader impacts, many challenges arise on the path from theory to deployment. This workshop will help researchers navigate this path by bringing together participants and speakers from academia, industry, and non-profits, highlighting successes going from theory to deployment, and facilitating collaboration.
- Promises and pitfalls: Advances on ML benchmarks do not always translate to improvements in computational sustainability problems, with contributing factors including low-signal-to-noise ratios, ever changing conditions, and biased or imbalanced data. However, due to the difficulties of publishing negative results, these findings rarely reach the community leading to duplicated effort and obscuring important gaps in existing methods.

The goals of this workshop are to (i) identify pathways from theory to deployment, including best-practices and measures to quantify success, (ii) facilitate discussion and collaboration between participants with diverse backgrounds, including academia, industry, and the non-profit sector, and (iii) identify common failure modes and high-impact research directions, including “moonshot’’ challenges.
# Workshop on Deep Learning and Inverse Problems

## Overview
Inverse problems are ubiquitous in science, medicine, and engineering, and research in this area has produced real-world impact in medical tomography, seismic imaging, computational photography, and other domains. The recent rapid progress in learning-based image generation raises exciting opportunities in inverse problems, and this workshop seeks to gather a diverse set of participants who apply machine learning to inverse problems, from mathematicians and computer scientists to physicists and biologists. This gathering will facilitate new collaborations and will help develop more effective, reliable, and trustworthy learning-based solutions to inverse problems.

## Topics

We welcome all submissions in the intersection of inverse problems and deep learning, including but not limited to submissions on the following topics:
- Fundamental approaches to address model uncertainty in learning-based solutions for inverse problems: Currently, the best DL-based solutions heavily rely on knowing the inverse system’s forward model and assume simple models of distortion (such as additive Gaussian noise). What algorithms and analysis techniques do we require for applications where we only have access to partial information about the system model?
- Diffusion models: Diffusion models have recently gained attention as powerful learned priors for solving inverse problems, due to their ability to model complex high-dimensional data across diverse modalities such as MRI, acoustics, graphs, proteins, etc. What are their benefits and limitations, and what are the optimal algorithms?
# Deep Generative Models for Health Workshop

## Overview
Deep generative models have recently gained unprecedented attention following recent advancements in text-to-image generation, diffusion models and large language models. Additionally, early well-established  approaches, such as variational autoencoders, generative adversarial networks, and normalizing flows, are widely applied for learning interpretable representations, as well as integrating multiple modalities or prior information from domain knowledge. These advancements hold the premise of unlocking significant potential in the health sector.

Generative AI emerges as a compelling solution in addressing the challenges posed by the scarcity of medical datasets due to complex data acquisition processes and privacy regulations, the demand for accountable and interpretable methodologies, and the need to integrate multiple and diverse modalities. Despite the recently witnessed methodological advances, generative approaches are limited in their current real-world medical applications. This is arguably due to several open challenges that include designing objective validation procedures, as well as finding reliable metrics for learnt representations to assess interpretability and semantic content.

In this workshop, we provide a unique venue for the most recent trends in research on deep generative models, focusing on exploring their potential for health applications. We also provide the optimal setting to discuss the open problems that prevent these methods from having a profound positive impact in clinical settings. This workshop will be the ideal venue to attract a diverse pool of researchers aiming to integrate generative models in health scenarios.

We encourage submissions that leverage the recent methodological advancements in generative models to address critical medical challenges across all data-types, paving the way for their practical integration into the healthcare system.

## Topics
We solicit original paper submissions advancing research that leverages methodological advancements in generative models to address health applications across all data-types. Under this premise, we encourage submissions touching topics such as (but not limited to):

- Synthetic data generation
- Combining multiple data modalities
- Super-resolution
- Scarcity/missingness of medical datasets
- Explainable and interpretable generative methods
- Robustness and validation procedures
- Advances in generative models tailored towards health applications, including but not limited to
    - text-to-image generation, 
    - diffusion models, 
    - large language models, 
    - variational autoencoders,
    - normalizing flows,
    - generative adversarial networks,
    
Finally, we encourage work that is actionable in clinical practice, especially targeting application areas that tackle minority data groups and, thus, have their own specific, often under-explored, challenges. Such areas include, but are not limited to, pediatrics, critical care (ICU), rare diseases like Alzheimer, HIV, and fertility.
# Workshop on Diffusion Models

## Overview

Over the past three years, diffusion models have established themselves as a new generative modelling paradigm. Their empirical successes have broadened the applications of generative modelling to image, video, audio, 3D synthesis and science applications. As diffusion models become more and more popular and are applied to extremely diverse problems, it also becomes harder to track the key contributions in the field. This workshop aims to keep track of the recent advances and set guidelines for future research. By bringing together practice, methodology and theory actors we aim at identifying unexplored areas and pushing the frontier of diffusion model research.

## Topics
We invite researchers from machine learning and related fields to submit their latest work on theory and applications of diffusion models to the workshop. We encourage submissions related (but not limited) to the following topics:

- Theory and methodology of diffusion models
    - Stochastic differential equations for generative models
    - Probabilistic inference, variational inference
    - Novel training methodology or architectures
    - Improved/accelerated diffusion model inference
    - Limitations and drawbacks of diffusion models
    - Theoretical properties of diffusion models
- Applications of diffusion models
    - Generation of images, video, audio, molecules, motion, etc.
    - Conditional generation, guidance, controllability, and personalization
    - 3D applications of diffusion models
    - Solving inverse problems
    - Image/video editing
    - Science and engineering applications
# The Symbiosis of Deep Learning and Differential Equations

In the deep learning community, a remarkable trend is emerging, where powerful architectures are created by leveraging classical mathematical modeling tools from diverse fields like differential equations, signal processing, and dynamical systems. Differential equations are a prime example: research on neural differential equations has expanded to include a large zoo of related models with applications ranging from time series analysis to robotics control. Score-based diffusion models are among state-of-the-art tools for generative modelling, drawing connections between diffusion models and neural differential equations. Other examples of deep architectures with important ties to classical fields of mathematical modelling include normalizing flows, graph neural diffusion models, Fourier neural operators, architectures exhibiting domain-specific equivariances, and latent dynamical models (e.g., latent NDEs, H3, S4, Hyena).

The previous two editions of the Workshop on the Symbiosis of Deep Learning and Differential Equations have promoted the bidirectional exchange of ideas at the intersection of classical mathematical modelling and modern deep learning. On the one hand, this includes the use of differential equations and similar tools to create neural architectures, accelerate deep learning optimization problems, or study theoretical problems in deep learning. On the other hand, the Workshop also explores the use of deep learning methods to improve the speed, flexibility, or realism of computer simulations.

# Topics
We invite high-quality extended abstract submissions on the intersection of DEs and DL, including but not limited to works that connect to this year's focus area of neural architectures that leverage classical mathematical models (see above). Some examples (non-exhaustive list):


- Using differential equation models to understand and improve deep learning algorithms:
    - Incorporating DEs into existing DL models (neural differential equations, diffusion models, ...)
    - Analysis of numerical methods for implementing DEs in DL models (trade-offs, benchmarks, ...)
    - Modeling training dynamics using DEs to generate theoretical insights and novel algorithms.
- Using deep learning algorithms to create or solve differential equation models:
    - DL methods for solving high-dimensional, highly parameterized, or otherwise challenging DE models.
    - Learning-augmented numerical methods for DEs (hypersolvers, hybrid solvers ...)
    - Specialized DL architectures for solving DEs (neural operators, PINNs, ...).
# Workshop on Gaze Meets ML

Eye gaze has proven to be a cost-efficient way to collect large-scale physiological data that can reveal the underlying human attentional patterns in real-life workflows and thus has long been explored as a signal to directly measure human-related cognition in various domains. Physiological data (including but not limited to eye gaze) offer new perception capabilities, which could be used in several ML domains, e.g., egocentric perception, embodied AI, NLP, etc. They can help infer human perception, intentions, beliefs, goals, and other cognition properties that are much needed for human-AI interactions and agent coordination. In addition, large collections of eye-tracking data have enabled data-driven modeling of human visual attention mechanisms, both for saliency or scanpath prediction, with twofold advantages: from the neuroscientific perspective to understand biological mechanisms better, from the AI perspective to equip agents with the ability to mimic or predict human behavior and improve interpretability and interactions. With the emergence of immersive technologies, now more than any time, there is a need for experts from various backgrounds (e.g., machine learning, vision, and neuroscience communities) to share expertise and contribute to a deeper understanding of the intricacies of cost-efficient human supervision signals (e.g., eye-gaze) and their utilization towards bridging human cognition and AI in machine learning research and development. The goal of this workshop is to bring together an active research community to collectively drive progress in defining and addressing core problems in gaze-assisted machine learning.

We welcome submissions that present aspects of eye gaze in regard to cognitive science, psychophysiology, and computer science or propose methods for integrating eye gaze into machine learning. We are also looking for applications from radiology, AR/VR, autonomous driving, etc. that introduce methods and models utilizing eye gaze technology in their respective domains.

# Topics

Topics of interest include but are not limited to the following:
- Understanding the neuroscience of eye-gaze and perception
- State of the art in incorporating machine learning and eye-tracking
- Annotation and ML supervision with eye-gaze
- Attention mechanisms and their correlation with eye-gaze
- Methods for gaze estimation and prediction using machine learning
- Unsupervised ML using eye gaze information for feature importance/selection
- Understanding human intention and goal inference
- Using saccadic vision for ML applications
- Use of gaze for human-AI interaction and agent coordination in multi-agent environments
- Eye gaze used for AI, e.g., NLP, Computer Vision, RL, Explainable AI, Embodied AI, Trustworthy AI
- Ethics of Eye Gaze in AI
- Gaze applications in cognitive psychology, radiology, neuroscience, AR/VR, autonomous cars, privacy, etc.
# Workshop on Goal-Conditioned Reinforcement Learning

Learning goal-directed behavior is one of the classical problems in AI, one that has received renewed interest in recent years and currently sits at the crossroads of many seemingly-disparate research threads: self-supervised learning , representation learning, probabilistic inference, metric learning, and duality. Our workshop focuses on these goal-conditioned RL (GCRL) algorithms and their connections to different areas of machine learning. Goal-conditioned RL is exciting not just because of these theoretical connections with different fields, but also because it promises to lift some of the practical challenges with applying RL algorithms: users can specify desired outcomes with a single observation, rather than a mathematical reward function. As such, GCRL algorithms may be applied to problems varying from robotics to language models tuning to molecular design to instruction following. Our workshop aims to bring together researchers studying the theory, methods, and applications of GCRL, researchers who might be well posed to answer questions such as: 
- How does goal-directed behavior in animals inform better GCRL algorithmic design? 
- How can GCRL enable more precise and customizable molecular generation? 
- Do GCRL algorithms provide an effective mechanism for causal reasoning? 
- When and how should GCRL algorithms be applied to precision medicine?

# Goal
The workshop aims to foster an inclusive environment where researchers and practitioners from all backgrounds can engage in discussions and build collaborations on the theory, methods, and applications of GCR. Broadly, the workshop will focus on the following topics and problems: 
- Connections : What are the connections between GCRL and representation learning, few-shot learning, and self-supervised learning? When does (say) effective representation learning emerge from GCRL? 
- Future directions : What are limitations of existing methods, benchmarks, and assumptions? 
- Algorithms : How might we improve existing methods, and do this in a way that enables applications to broader domains (e.g., molecular discovery, instruction-following robots)?

# Topics 
We solicit submissions related to (but not limited to) the following topics:
- Algorithms. We encourage both proposals of new methods, as well as analyses and/or evaluations of existing ones.
Connections between goal-conditioned RL and other ML areas.
- Examples might include representation learning, self-supervised learning, adversarial training, probabilistic inference, metric learning, duality, etc.
- Applications of goal-conditioned decision making.
In addition to common decision making tasks (e.g., robotics and games) and goal-conditioned applications (e.g. instruction-following, molecular discovery), we especially encourage work in goal-conditioned domains where GCRL is not (yet)
the mainstream strategy
# Generative AI and Biology (GenBio) Workshop

Over the past year, generative AI models have led to tremendous breakthroughs, from image and text generation, to protein folding and design. These recent successes illustrate the incredible potential of generative AI not only for digital applications, but also for basic science and healthcare. We are now able to predict protein structure from sequence alone; to characterize the function and interactions of biomolecules; to design such molecules never-before-seen in nature; and more. The impacts are profound: through generative AI, we can systematically understand and reprogram biology at an unprecedented level.

# Topics
The scope of this workshop includes, but not limited to, the following topics.
- Designing and optimizing novel and useful biomolecules
    - Rational protein design: Prediction and optimization of protein sequences and/or structures, incorporating constraints and prior knowledge
    - Small molecule drug design: Discovery and optimization of novel and effective small molecule therapeutics, incorporating information about the biological context
    - Next frontiers of de-novo design: Designing other biomolecules including peptides, oligonucleotides, antibodies, or targeted degraders 
- From first principles: generative modeling for biological data
    - Sequence-based methods: large language models for protein / genomic sequences, sequence-based molecular design
    - Graph-based methods: generative learning on biological graphs and networks, e.g., molecular graphs, protein-protein interaction networks, genome-wide association graphs
    - Geometric deep learning: generative modeling of biological structures as point clouds, surfaces, and other geometric objects
- Open challenges in generative AI and biology (Special Track)
    - Large language models for scientific discovery: literature summarization, structured information extraction, identifying knowledge gaps and uncovering novel connections, formulation of scientific hypotheses
    - Finding common ground: systematic barriers, biological experiment design with GenerativeAI-in-the-loop Identifying the right problems: pressing challenges in biology that are difficult to address via traditional means, gap between biological need and existing generative algorithms