task_id int64 0 200 | task_name stringlengths 11 34 | task_description stringlengths 605 7.73k |
|---|---|---|
0 | iclr2023_bands | # Backdoor Attacks and Defenses in Machine Learning
## Overview
Backdoor attacks aim to cause consistent misclassification of any input by adding a specific pattern called a trigger. Unlike adversarial attacks requiring generating perturbations on the fly to induce misclassification for one single input, backdoor att... |
1 | iclr2023_dg | # What do we need for successful domain generalization?
## Workshop Description
The real challenge for any machine learning system is to be reliable and robust in any situation, even if it is different compared to training conditions. Existing general purpose approaches to domain generalization (DG) — a problem setti... |
2 | iclr2023_ml4materials |
# Machine Learning for Materials
## Overview
Many of the world's most crucial challenges, such as access to renewable energy, energy storage, or clean water, are currently fundamentally bottlenecked by materials challenges. The discovery of new materials drives the development of key technologies like solar cells, b... |
3 | iclr2023_mlgh | ## Machine Learning & Global Health
During the Covid-19 pandemic, in spite of the impressive advances in machine learning in recent decades, the successes of this field were modest at best. Much work remains, for both machine learning and global health researchers, to deliver true progress in global health. This works... |
4 | iclr2023_mrl | # Multimodal Representation Learning: Perks and Pitfalls
## About the workshop
Following deep learning, multimodal machine learning has made steady progress, becoming ubiquitous in many domains. Learning representations from multiple modalities can be beneficial since different perceptual modalities can inform each o... |
5 | iclr2023_nf | ## Neural Fields across Fields: Methods and Applications of Implicit Neural Representations
Addressing problems in different science and engineering disciplines often requires solving optimization problems, including via machine learning from large training data. One class of methods has recently gained significant at... |
6 | iclr2023_physics4ml | ## Physics for Machine Learning
Combining physics with machine learning is a rapidly growing field of research. Thus far, most of the work in this area focuses on leveraging recent advances in classical machine learning to solve problems that arise in the physical sciences. In this workshop, we wish to focus on a slig... |
7 | iclr2023_rrl | ## Reincarnating RL
This inaugural workshop at ICLR 2023 (in-person) aims to bring further attention to the emerging paradigm of reusing prior computation in RL, which we refer to as reincarnating RL. Specifically, we plan to discuss potential benefits of reincarnating RL, its current limitations and associated challe... |
8 | iclr2023_rtml | ## Trustworthy and Reliable Large-Scale Machine Learning Models
In recent years, the landscape of AI has been significantly altered by the advances in large-scale pre-trained models. Scaling up the models with more data and parameters has significantly improved performance and achieved great success in a variety of ap... |
9 | iclr2023_snn | ## Overview of Sparsity in Neural Networks
Deep networks with billions of parameters trained on large datasets have achieved unprecedented success in various applications, ranging from medical diagnostics to urban planning and autonomous driving, to name a few. However, training large models is contingent on exception... |
10 | iclr2023_sr4ad | ## Overview of Scene Representations for Autonomous Driving
This workshop aims to promote the real-world impact of ML research toward self-driving technology. While ML-based components of modular stacks have been a huge success, there remains progress to be made in the development of integration strategies and interme... |
11 | iclr2023_tml4h | ## Trustworthy Machine Learning for Healthcare Workshop
Machine learning (ML) has achieved or even exceeded human performance in many healthcare tasks, owing to the fast development of ML techniques and the growing scale of medical data. However, ML techniques are still far from being widely applied in practice. Real-... |
12 | iclr2023_trustml | ## Pitfalls of limited data and computation for Trustworthy ML
Due to the impressive performance of ML algorithms, they are increasingly used in a wide range of applications that impact our daily lives. These include sensitive domains like healthcare, banking, social services, autonomous transportation, social media, ... |
13 | iclr2023_tsrl4h | ## Workshop on Time Series Representation Learning for Health
Time series data have been used in many applications in healthcare, such as the diagnosis of a disease, prediction of disease progression, clustering of patient groups, online monitoring, and dynamic treatment regimes, to name a few. More and more methods b... |
14 | iclr2024_agi | # How Far Are We From AGI
## Topics
This workshop aims to become a melting pot for ideas, discussions, and debates regarding our proximity to AGI. We invite submissions on a range of topics including, but not limited to:
1. **Frontiers of AGI research:** Examples include AI agents, embodied AI, retrieval-based and t... |
15 | iclr2024_al4de | # AI4DifferentialEquations In Science
## Background
Over the past decade, the integration of Artificial Intelligence (AI) for scientific exploration has grown as a transformative force, propelling research into new realms of discovery. The AI4DifferentialEquations in Science workshop at ICLR 2024 invites participants... |
16 | iclr2024_bgpt | ## Bridging the Gap Between Practice and Theory in Deep Learning
The success of deep learning practices has driven the rapid development of learning theory. However, recent studies have pointed out that contrasting scenarios and conclusions exist between many existing theories and their corresponding real-world applic... |
17 | iclr2024_dmlr | ## Data-centric Machine Learning Research
Large-scale foundation models are revolutionizing machine learning, particularly in vision and language domains. While model architecture received significant attention in the past, recent focus has shifted towards the importance of data quality, size, and diversity, and prove... |
18 | iclr2024_dpfm | # Navigating and Addressing Data Problems for Foundation Models
## Overview
Foundation Models (FMs, e.g., GPT-3/4, LLaMA, DALL-E, Stable Diffusion, etc.) have demonstrated unprecedented performance across a wide range of downstream tasks. Following the rapid evolution, as researchers strive to keep up with the unders... |
19 | iclr2024_gem | # Generative and Experimental Perspectives for Biomolecular Design
## About
Biomolecular design, through artificial engineering of proteins, molecules, and nucleic acids, holds immense promise in addressing pressing medical, industrial, and environmental challenges. While generative machine learning has shown signifi... |
20 | iclr2024_genai4dm | ## Generative Models for Decision Making
Generative Artificial Intelligence (AI) has made significant advancements in recent years, particularly with the development of large language and diffusion models. These generative models have demonstrated impressive capabilities across various domains, such as text, image, au... |
21 | iclr2024_globalai | # Global AI Cultures
## Description
Building globally-inclusive artificial intelligence systems that encodes and respects cultural sensibilities as well as performs well for users across cultural contexts, is an important goal as we deploy AI products globally. However existing AI evaluation, design and deployment pr... |
22 | iclr2024_llm4agents | # Large Language Model (LLM) Agents
## About
This Workshop delves into the significance of agents driven by large language models (LLMs), a topic that has recently sparked intense discussions.
Building on the current huge progress on LLMs, we'll focus on autonomous agents that perform intricate tasks in both real an... |
23 | iclr2024_mefomo | ## Workshop on Mathematical and Empirical Understanding of Foundation Models
Foundation models (FMs) have revolutionized machine learning research across domains. These models are trained on extensive, highly varied datasets and can be quickly adapted to solve many tasks of interest. FMs are extremely effective on lan... |
24 | iclr2024_mlgenx | # Machine Learning for Genomics Explorations
## Overview
Our limited understanding of the biological mechanisms underlying diseases remains a critical bottleneck in drug discovery. As a result, we often lack insights into why patients develop specific conditions, leading to the failure of many drug candidates in clin... |
25 | iclr2024_pml | # Privacy Regulation and Protection in Machine Learning
## Introduction
Recent advances in artificial intelligence greatly benefit from data-driven machine learning methods that train deep neural networks with large scale data. The usage of data should be responsible, transparent, and comply with privacy regulations.... |
26 | iclr2024_pml4lrs | # Practical ML for Limited/Low Resource Settings
## Introduction
The constant progress being made in machine learning needs to extend across borders if we are to democratize ML in developing countries. Adapting state-of-the-art (SOTA) methods to resource constrained environments such as developing countries can be c... |
27 | iclr2024_r2fm | # Reliable and Responsible Foundation Models
## Overview
In the era of AI-driven transformations, foundation models (FMs), like large-scale language and vision models, have become pivotal in various applications, from natural language processing to computer vision. These models, with their immense capabilities, offer... |
28 | iclr2024_realign | # Workshop on Representational Alignment
## About
Both natural and artificial intelligences form representations of the world that they use to reason, make decisions, and communicate. Despite extensive research across machine learning, neuroscience, and cognitive science, it remains unclear what the most appropriate ... |
29 | iclr2024_setllm | # Workshop on Secure and Trustworthy Large Language Models
## About
The striding advances of large language models (LLMs) are revolutionizing many long-standing natural language processing tasks ranging from machine translation to question-answering and dialog systems. However, as LLMs are often built upon massive am... |
30 | iclr2024_ts4h | # Learning from Time Series for Health
Time series data are ubiquitous in healthcare, from medical time series to wearable data, and present an exciting opportunity for machine learning methods to extract actionable insights about human health. However, huge gap remain between the existing time series literature and w... |
31 | iclr2025_agenticai | # Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation
## About the Workshop
Our mission is to foster interdisciplinary collaboration to develop fully autonomous AI systems, addressing challenges like benchmark datasets, human-AI collaboration, robust tools and methods f... |
32 | iclr2025_ai4chl | # AI for Children: Healthcare, Psychology, Education
## About the Workshop
Current AI research and applications often prioritize adult-focused solutions, while progress in AI designed specifically for children’s development, health, and education has lagged behind. Our workshop aims to spotlight this issue and bring t... |
33 | iclr2025_ai4mat | ## About the Workshop
The AI for Accelerated Materials Discovery (AI4Mat) Workshop NeurIPS 2024 provides an inclusive and collaborative platform where AI researchers and material scientists converge to tackle the cutting-edge challenges in AI-driven materials discovery and development. Our goal is to foster a vibrant e... |
34 | iclr2025_ai4na | # Workshop on AI for Nucleic Acids
AI4NA aims to popularize AI applications for nucleic acids and introduce nucleic acid research challenges to the broader AI community. This workshop aims to spotlight nucleic acids as the next frontier for AI research. By bringing together experts from machine learning and biology, w... |
35 | iclr2025_bi_align | # Workshop on Bidirectional Human-AI Alignment
This workshop focuses on bidirectional Human AI alignment, a paradigm shift in how we approach the challenge of human-AI alignment, which emphasizes the dynamic, complex, and evolving alignment process between humans and AI systems. This is grounded on the "bidirectional ... |
36 | iclr2025_buildingtrust | # Workshop on Building Trust in Language Models and Applications
As Large Language Models (LLMs) are rapidly adopted across diverse industries, concerns around their trustworthiness, safety, and ethical implications increasingly motivate academic research, industrial development, and legal innovation. LLMs are increas... |
37 | iclr2025_data_problems | # Workshop on Navigating and Addressing Data Problems for Foundation Models
Foundation models (FMs) have become central to modern machine learning, with data playing a crucial role in their development and sparking increased attention to data-related challenges such as curation and attribution. Adapting traditional da... |
38 | iclr2025_delta | # Workshop on Deep Generative Model in Machine Learning: Theory, Principle and Efficacy
We are excited to invite submissions to the ICLR 2025 Workshop on Deep Generative Models: Theory, Principle, and Efficacy. This workshop aims to explore challenges and opportunities in advancing the theoretical foundations and prac... |
39 | iclr2025_dl4c | The thrid DL4C workshop titled "Emergent Possibilities and Challenges in Deep Learning for Code" provides a vibrant platform for researchers to share their work on deep learning for code, emphasizing emergent possibilities and challenges, for example: agentic methods for programming tasks, post-training and alignment f... |
40 | iclr2025_embodiedai | # Workshop on Embodied Intelligence with Large Language Models In Open City Environment
This workshop is motivated by a fact: human beings have strong embodied intelligence in an open environment, but it is still challenging for large language models and LLM agents. Depsite some progresses on embodied AI on static and... |
41 | iclr2025_financial_ai | # Workshop on Advances in Financial AI: Opportunities, Innovations and Responsible AI
The financial industry is undergoing a transformative shift fueled by rapid advancements in artificial intelligence. From algorithmic trading and fraud detection to personalized banking and investment strategies, AI is redefining how... |
42 | iclr2025_fm_wild | # Workshop on Foundation Models in the Wild
In the era of AI-driven transformations, foundation models (FMs) have become pivotal in various applications, from natural language processing to computer vision. These models, with their immense capabilities, reshape the future of scientific research and the broader human s... |
43 | iclr2025_fpi | # Frontiers in Probabilistic Inference: Learning meets Sampling
## About the Workshop
The Frontiers in Probabilistic Inference: Sampling meets Learning (FPI) workshop at ICLR 2025 focuses on modern approaches to probabilistic inference to address the challenging and under-explored area of sampling from an unnormalized... |
44 | iclr2025_gem | # Workshop on Generative and Experimental Perspectives for Biomolecular Design
Biomolecular design, through artificial engineering of proteins, molecules, and nucleic acids, holds immense promise in addressing pressing medical, industrial, and environmental challenges. While generative machine learning has shown signi... |
45 | iclr2025_haic | HAIC 2025, the First Workshop on Human-AI Coevolution, focuses on the emerging field of Human-AI Coevolution (HAIC) to understand the feedback loops that emerge through continuous human-AI coadaptation.
This workshop focuses on new approaches beyond AI performance benchmarks, exploring multiple levels of analysis span... |
46 | iclr2025_icbinb | # I Can't Believe It's Not Better: Challenges in Applied Deep Learning
Why don’t deep learning approaches always deliver as expected in the real world?
Dive deep into the pitfalls and challenges of applied deep learning.
In recent years, we have witnessed a remarkable rise of deep learning (DL), whose impressive per... |
47 | iclr2025_llm_reason_and_plan | # Workshop on Reasoning and Planning for Large Language Models
## About The Workshop
This workshop explores the growing capabilities of large language models (LLMs), such as OpenAI's o1 model, in reasoning, planning, and decision-making, highlighting recent advances and challenges. We aim to examine how reinforcement ... |
48 | iclr2025_lmrl | # Learning Meaningful Representations of Life (LMRL)
## About this workshop
Since the last time that the LMRL workshop was held at NeurIPS 2022, interest in representation learning for biology has surged, with new ideas challenging traditional approaches and sparking discussions on how best to capture the complexity ... |
49 | iclr2025_mcdc | # Workshop on Modularity for Collaborative, Decentralized, and Continual Deep Learning
## Summary
While the success of large-scale deep learning models has hinged on the ``bigger is better'' approach – scaling model size and training data – this paradigm may rapidly be reaching an inflection point. Beyond the prohib... |
50 | iclr2025_mldpr | # The Future of Machine Learning Data Practices and Repositories
## About this workshop
Datasets are a central pillar of machine learning (ML) research—from pretraining to evaluation and benchmarking. However, a growing body of work highlights serious issues throughout the ML data ecosystem, including the under-valui... |
51 | iclr2025_mlgenx | # Workshop on Machine Learning for Genomics Explorations
Our limited understanding of the biological mechanisms underlying diseases remains a critical bottleneck in drug discovery. As a result, we often lack insights into why patients develop specific conditions, leading to the failure of many drug candidates in clini... |
52 | iclr2025_mlmp | # Workshop on Machine Learning Multiscale Processes
Given low-level theory and computationally-expensive simulation code, how can we model complex systems on a useful time scale?
Fundamental laws of Nature, Standard Model of Physics, and the most applied part of it, quantum mechanics, are well established. Theoretica... |
53 | iclr2025_nfam | # New Frontiers in Associative Memories
## About This Workshop
Associative Memory (AM) is a core notion in psychology responsible for our ability to link people's names to their faces and to remember the smell of a strawberry when we see one. Mathematical formalizations of AM date back to the 1960s-1980s [...] . For i... |
54 | iclr2025_question | # Quantify Uncertainty and Hallucination in Foundation Models: The Next Frontier in Reliable AI
How can we trust large language models (LLMs) when they generate text with confidence, but sometimes hallucinate or fail to recognize their own limitations? As foundation models like LLMs and multimodal systems become perva... |
55 | iclr2025_re_align | # Representational Alignment
Both natural and artificial intelligences form representations of the world that they use to reason, make decisions, and communicate. Despite extensive research across machine learning, neuroscience, and cognitive science, it remains unclear what the most appropriate ways are to compare an... |
56 | iclr2025_sci_fm | # Workshop on Open Science for Foundation Models
## About the Workshop
Foundation models (FMs) have transformed AI research but lack scientific transparency. The SCI-FM workshop aims to address this by fostering open science, reproducibility, and the sharing of open-source models and datasets. We invite contributions ... |
57 | iclr2025_scope | # Workshop on Scalable Optimization for Efficient and Adaptive Foundation Models
## About This Workshop
In the rapidly evolving landscape of AI, the development of scalable optimization methods to yield efficient and adaptive foundation models has significant demand in the space of their inference service. In specifi... |
58 | iclr2025_scsl | ## Workshop on Spurious Correlation and Shortcut Learning: Foundations and Solutions
Reliance on spurious correlations due to simplicity bias is a well-known pitfall of deep learning models. This issue stems from the statistical nature of deep learning algorithms and their inductive biases at all stages, including dat... |
59 | iclr2025_sllm | ## Deep Dive into Mixture of Experts, Quantization, Hardware, and Inference
Large Language Models (LLMs) have emerged as transformative tools in both research and industry, excelling across a wide array of tasks. However, their growing computational demands especially during inference—raise significant concerns about ... |
60 | iclr2025_ssi_fm | # Scaling Self-Improving Foundation Models without Human Supervision
## Overview
The availability of internet data, while vast, is ultimately finite or at least growing at a pace that lags behind the consumption needs of foundation models (FMs) during pre-training. Perhaps as is most evident with large language model... |
61 | iclr2025_synthdata | # Will Synthetic Data Finally Solve the Data Access Problem?
Accessing large scale and high quality data has been shown to be one of the most important factors to the performance of machine learning models. Recent works show that large (language) models can greatly benefit from training with massive data from diverse (... |
62 | iclr2025_verifai | # VerifAI: AI Verification in the Wild
## Overview
This workshop explores the intersection of scale-driven generative artificial intelligence (AI) and the correctness-focused principles of verification.
Formal analysis tools such as theorem provers, satisfiability solvers, and execution monitoring have demonstrated s... |
63 | iclr2025_wmark | # Workshop on GenAI Watermarking
A Dedicated Space for Watermarking in Generative AI
Watermarking is crucial in the age of generative AI, yet often gets lost in broader conversations around AI safety and security. Our workshop brings together experts from academia, industry, policy, and different communities to discu... |
64 | iclr2025_world_models | # Workshop on World Models: Understanding, Modelling and Scaling
## Workshop Scope
The concept of the "World Model" focuses on how intelligent agents can understand and model the external interactive worlds/environments to improve their decision-making and planning abilities. World models were initially focused on mo... |
65 | iclr2025_wrl | # Robot Learning Workshop: Towards Robots with Human-Level Abilities
## About This Workshop
The year 2024 has seen an explosion of interest in humanoid robots. However, recent systems for drone racing, playing table tennis, and others clearly demonstrate that the humanoid form-factor isn’t a requirement for human-leve... |
66 | iclr2025_wsl | # Workshop on Neural Network Weights as a New Data Modality
## Overview
The recent surge in the number of publicly available neural network models—exceeding a million on platforms like Hugging Face—calls for a shift in how we perceive neural network weights. This workshop aims to establish neural network weights as a ... |
67 | iclr2025_xai4science | # XAI4Science: From Understanding Model Behavior to Discovering New Scientific Knowledge
## About This Workshop
Machine learning (ML) models are impressive when they work but they can also show unreliable, untrustworthy, and harmful dangerous behavior. Yet, such models are widely adopted and deployed, even though we d... |
68 | icml2023_aihci | # Artificial Intelligence and Human-Computer Interaction
## Workshop Overview
Artificial intelligence (AI) and Human-Computer Interaction (HCI) share common roots: early work on conversational agents has laid the foundation for both fields. Despite sharing a common history, economic and political influences have driv... |
69 | icml2023_deploygenai | ## Challenges of Deploying Generative AI
Generative modeling has recently gained massive attention given high-profile successes in natural language processing and computer vision. However, there remain major challenges in deploying generative models for real-world impact in domains like healthcare and biology. This is... |
70 | icml2023_differentiable | ## Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators
Gradients and derivatives are integral to machine learning, as they enable gradient-based optimization. In many real applications, however, models rest on algorithmic components that implement discrete decisions, or ... |
71 | icml2023_dp4ml | ## Duality Principles for Modern Machine Learning
The ICML Duality Principles workshop brings together researchers working on various duality concepts from many different fields to discuss new applications for modern machine learning, especially focusing on topics such as model understanding, explanation, and adaptati... |
72 | icml2023_fl | # Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities
## About the Workshop
Proposed in 2016 as a privacy enhancing technique, federated learning and analytics (FL & FA) made remarkable progress in theory and practice in recent years. However, there is a growing disconne... |
73 | icml2023_frontiers4lcd | ## New Frontiers in Learning, Control, and Dynamical Systems
Recent advances in algorithmic design and principled, theory-driven deep learning architectures have sparked a growing interest in control and dynamical system theory. Complementary, machine learning plays an important role in enhancing existing control theo... |
74 | icml2023_ilhf | ## Interactive Learning with Implicit Human Feedback
Systems that can learn interactively from their end-users are quickly becoming widespread in real-world applications. Typically humans provide tagged rewards or scalar feedback for such interactive learning systems. However, humans offer a wealth of implicit informa... |
75 | icml2023_imlh | ## Interpretable Machine Learning in Healthcare
Applying machine learning (ML) in healthcare is gaining momentum rapidly. However, the black-box characteristics of the existing ML approach inevitably lead to less interpretability and verifiability in making clinical predictions. To enhance the interpretability of med... |
76 | icml2023_llw | ## Localized Learning Workshop
Despite being widely used, global end-to-end learning has several key limitations. It requires centralized computation, making it feasible only on a single device or a carefully synchronized cluster. This restricts its use on unreliable or resource-constrained devices, such as commodity ... |
77 | icml2023_mfpl | ## The Many Facets of Preference-based Learning
Learning from human preferences, or preference-based learning, has been critical to major advances in AI and machine learning. It is based on the fact that humans are more reliable at providing relative feedback compared to numerical values. Therefore, preference feedbac... |
78 | icml2023_ncw | # Neural Compression: From Information Theory to Applications
## Overview
The workshop solicits original research in the intersection of machine learning, data/model compression, and more broadly information theory.
Machine learning and compression have been described as “two sides of the same coin”, and the exponen... |
79 | icml2023_pac |
# PAC-Bayes Meets Interactive Learning Workshop
## Scope
Interactive learning encompasses online learning, continual learning, active learning, bandits, reinforcement learning, and other settings where an algorithm must learn while interacting with a continual stream of data. Such problems often involve exploration-... |
80 | icml2023_scis | ## Workshop on Spurious Correlations, Invariance and Stability
The workshop brings together domain experts and researchers to facilitate discussions and forge collaborations on problems with spurious correlations, and instability of machine learning models. Models built without accounting for spurious correlations oft... |
81 | icml2023_sods | # Sampling and Optimization in Discrete Space
## Overview
Sampling and optimization in discrete space are classical and important problems that arise in many applications, including physics, combinatorial optimization, compiler optimization, and many modern machine learning models like large language models and prote... |
82 | icml2023_spigm | ## Structured Probabilistic Inference & Generative Modeling
Probabilistic inference addresses the problem of amortization, sampling, and integration of complex quantities from graphical models, while generative modeling captures the underlying probability distributions of a dataset. Despite promising results, probabil... |
83 | icml2023_synsml | # Workshop on the Synergy of Scientific and Machine Learning Modeling
## Introduction
The Synergy of Scientific and Machine Learning Modeling Workshop (“SynS & ML”) is an interdisciplinary forum for researchers and practitioners interested in the challenges of combining scientific and machine-learning models. The goa... |
84 | icml2023_tagml | ## Topology, Algebra, and Geometry in Machine Learning Workshop
Much of the data that is fueling current rapid advances in machine learning is high dimensional, structurally complex, and strongly nonlinear. This poses challenges for researcher intuition when they ask (i) how and why current algorithms work and (ii) wh... |
85 | icml2023_tom | # Workshop on Theory of Mind in Communicating Agents
## Motivation
Theory of Mind (ToM) is the ability to reason about the minds of other agents. The main theme of our workshop is the computational modeling of ToM, with a special focus on the role of natural language in such modeling. Specifically, ToM 2025 pays atte... |
86 | icml2024_FoRLaC | # Foundations of RL and Control: Connections and New Perspectives
## About
Despite rapid advances in machine learning, solving large-scale stochastic dynamic programming problems remains a significant challenge. The combination of neural networks with RL has opened new avenues for algorithm design, but the lack of th... |
87 | icml2024_accml | # Workshop on Efficient and Accessible Foundation Models for Biological Discovery
## About
There is a growing gap between machine learning (ML) research on biology-inspired problems and the actual broad-based use of ML in the lab or the clinic. This gap is especially pressing in the context of foundation models and o... |
88 | icml2024_ai4math | # AI for Math
## Workshop Summary
Mathematical reasoning is one of the most advanced forms of human intelligence. Humans develop formal languages for rigorously describing mathematical problems and deriving mathematical knowledge. The machine learning community has endeavored to develop neural models with mathematica... |
89 | icml2024_ai4science | # AI for Science: Scaling in AI for Scientific Discovery
## About
Dramatic developments in AI have led to its increasing adoption in science as a means to model complex phenomena, generate hypotheses, design experiments, collect and interpret large datasets, and gain new insights that might not have been possible usi... |
90 | icml2024_alignRL | # Aligning Reinforcement Learning Experimentalists and Theorists
## Overview
Recent progress in reinforcement learning (RL) has powered breakthroughs in various real-world problems, gathering considerable attention and investment. However, it has also exposed a significant gap between theoretical and experimental dev... |
91 | icml2024_autorl | # Automated Reinforcement Learning: Exploring Meta-Learning, AutoML, and LLMs
## About
The past few years has seen a surge of interest in reinforcement learning, , with breakthrough successes of applying RL in games, robotics, chemistry, logistics, nuclear fusion and more. These headlines, however, blur the picture o... |
92 | icml2024_dmlr | # Workshop on Data-Centric Machine Learning Research
## Scope
Large-scale foundation models are revolutionizing machine learning, particularly in vision and language domains. While model architecture received significant attention in the past, recent focus has shifted towards the importance of data quality, size, an... |
93 | icml2024_fminwild | # Workshop on Foundation Models in the Wild
## Abstract
In the era of AI-driven transformations, foundation models (FMs), like large-scale language and vision models, have become pivotal in various applications, from natural language processing to computer vision. These models, with their immense capabilities, reshap... |
94 | icml2024_gram | # Workshop on Geometry-grounded Representation Learning and Generative Modeling
## Motivation
By recognizing that nearly all data is rooted in our physical world, and thus inherently grounded in geometry and physics, it becomes evident that representation learning should preserve this grounding in order to remain mea... |
95 | icml2024_hdlearning | # High-dimensional Learning Dynamics 2024: The Emergence of Structure and Reasoning
## About
The unprecedented scale and complexity of modern neural networks have revealed emergent patterns in learning dynamics and scaling behaviors. Recent advances in analyzing high-dimensional systems have uncovered fundamental rel... |
96 | icml2024_humans_algs_society | # Workshop on Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact
## About
With the widespread adoption of machine learning in social technologies, there are increasingly complex interactions between humans, algorithmic decision-makers, and society at large. For instance, algorithmic dec... |
97 | icml2024_icl | ## About
In-context learning (ICL) is an emerging capability of large-scale models, including large language models (LLMs) like GPT-3, to acquire new capabilities directly from the context of an input example without separate training or fine-tuning, enabling these models to adapt rapidly to new tasks, datasets, and d... |
98 | icml2024_lcfm | # Workshop on Long-Context Foundation Models
## Overview
Many challenging tasks for foundation models require synthesizing information over thousands to millions of individual pieces of data, which may take many forms, including images, text, audio, genomes, etc. Our workshop aims to convene researchers to address ch... |
99 | icml2024_llmandcog | # Workshop on LLMs and Cognition
## About
Large Language Models (LLMs) have undoubtedly taken center stage in the AI revolution, showing impressive performance in a wide variety of tasks, including machine translation, standardized tests, and conversational chatbots. It is even more impressive to uncover that these m... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.