task_id int64 0 200 | task_name stringlengths 11 34 | task_description stringlengths 605 7.73k |
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100 | icml2024_mfmeai | # Multi-modal Foundation Model meets Embodied AI
## Overview
Multi-modal Foundation Model meets Embodied AI (MFM-EAI)In recent years, Multi-modal Foundation Models (MFM) such as CLIP, ImageBind, DALL·E 3, GPT-4V, and Gemini have emerged as one of the most captivating and rapidly advancing areas in AI, drawing signif... |
101 | icml2024_mi | # Workshop on Mechanistic Interpretability
## Overview
Aligning AI agents with human intentions and values is one of the main barriers to the safe and ethical application of AI systems in the real world, spanning various domains such as robotics, recommender systems, autonomous driving, and large language models. To ... |
102 | icml2024_ml4earthsys | # Workshop on Machine Learning for Earth System Modeling
## Summary
Climate change is a major concern for human civilization, yet significant uncertainty remains in future warming, change in precipitation patterns, and frequency of climate extremes. Proper adaptation and mitigation demands accurate climate projectio... |
103 | icml2024_ml4lms | # Workshop ML for Life and Material Science: From Theory to Industry Applications
## Overview
This workshop aims to highlight translational ML research in biology and chemistry ML for real-world applications in life-and materials science. The goal is to bridge theoretical advanceswith practical applications and conne... |
104 | icml2024_nextgenaisafety | # Next Generation of AI Safety
## Overview
In recent years, general-purpose AI has experienced a meteoric rise in capabilities and applications. This rise has continued to bring forth new safety challenges, requiring mitigation to ensure AI systems meet trustworthiness standards.
In this workshop, we take a proacti... |
105 | icml2024_nxgenseqm | # Next Generation of Sequence Modeling Architectures Workshop at ICML 2024
## Description
This workshop will bring together various researchers to chart the course for the next generation of sequence modeling architectures. The focus will be on better understanding the limitations of existing models like transformers... |
106 | icml2024_spigm | # Workshop on Structured Probabilistic Inference & Generative Modeling
## Overview
The workshop focuses on theory, methodology, and application of structured probabilistic inference and generative modeling Probabilistic inference addresses the problem of amortization, sampling, and integration of complex quantities f... |
107 | icml2024_tf2m | # Workshop on Theoretical Foundations of Foundation Models
## Summary
Recent advancements in generative foundation models (FMs) such as large language models (LLMs) and diffusion models have propelled the capability of deep neural models to seemingly magical heights. Yet, the soaring growth in the model size and capa... |
108 | icml2024_tifa | # Trustworthy Multi-modal Foundation Models and AI Agents (TiFA)
## Descriptions
Advanced Multi-modal Foundation Models (MFMs) and AI Agents, equipped with diverse modalities and an increasing number of available affordances (e.g., tool use, code interpreter, API access, etc.), have the potential to accelerate and am... |
109 | icml2024_want | # 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 a... |
110 | neurips2023_ai4d3 | # 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... |
111 | neurips2023_ai4science | # 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 o... |
112 | neurips2023_aloe | # Agent Learning in Open-Endedness Workshop
# About
Rapid progress in sequential decision-making via deep reinforcement learning (RL) and, more recently, large language models (LLMs) has resulted in agents capable of succeeding in increasingly challenging tasks. However, once the agent masters the task, the learning ... |
113 | neurips2023_compsust | # 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 ... |
114 | neurips2023_crl | # Causal Representation Learning Workshop
## About the workshop
Current machine learning systems have rapidly increased in performance by leveraging ever-larger models and datasets. Despite astonishing abilities and impressive demos, these models fundamentally only learn from statistical correlations and struggle at t... |
115 | neurips2023_deep_inverse | # 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 ... |
116 | neurips2023_dgm4h | # 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, generat... |
117 | neurips2023_diffusion | # 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 model... |
118 | neurips2023_distshift | # Workshop on Distribution Shifts: New Frontiers with Foundation Models
## Overview
This workshop focuses on distribution shifts in the context of foundation models.
Distribution shifts—where a model is deployed on a data distribution different from what it was trained on—pose significant robustness challenges in real... |
119 | neurips2023_dlde | # 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. Differentia... |
120 | neurips2023_federated_learning | # Federated Learning in the Age of Foundation Models
Training machine learning models in a centralized fashion often faces significant challenges due to regulatory and privacy concerns in real-world use cases. These include distributed training data, computational resources to create and maintain a central data reposi... |
121 | neurips2023_fmdm | # Foundation Models for Decision Making
Foundation models pretrained on diverse vision and language datasets have demonstrated exceptional capabilities in performing a wide range of downstream vision and language tasks. As foundation models are deployed in real-world applications such as dialogue, autonomous driving, ... |
122 | neurips2023_gaied | # Workshop on Generative AI for Education (GAIED)
GAIED (pronounced "guide") aims to bring together researchers, educators, and practitioners to explore the potential of generative AI for enhancing education. Such an exploration, jointly as a community, is time critical: Recent advances in generative AI, in particular... |
123 | neurips2023_gaze_meets_ml | # 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... |
124 | neurips2023_gcrl | # 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, pro... |
125 | neurips2023_genbio | # 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 s... |
126 | neurips2023_genplan | # Workshop on Generalization in Planning
Humans are good at solving sequential decision-making problems, generalizing from a few examples, and learning skills that can be transferred to solve unseen problems. However, these problems remain long-standing open problems in AI.
This workshop will feature a synthesis of t... |
127 | neurips2023_glfrontiers | # New Frontiers in Graph Learning
Graph learning has grown into an established sub-field of machine learning in recent years. Researchers have been focusing on developing novel model architectures, theoretical understandings, scalable algorithms and systems, and successful applications across industry and science rega... |
128 | neurips2023_heavytails | # Heavy Tails in Machine Learning
Heavy-tailed distributions likely produce observations that can be very large in magnitude and far from the mean; hence, they are often used for modeling phenomena that exhibit outliers. As a consequence, the machine learning and statistics communities often associate heavy-tailed beh... |
129 | neurips2023_infocog | # Information-Theoretic Principles in Cognitive Systems
The InfoCog workshop is an interdisciplinary venue for exploring new avenues for progress toward an integrative computational theory of human and artificial cognition, by leveraging information-theoretic principles and formulations. To this end, we aim to bring t... |
130 | neurips2023_instruction | # Instruction Tuning and Instruction Following
Recent advancements in training large language models (LLMs) to follow “instructions” have significantly increased their ability to comprehend open-ended language commands, encompassing a wide range of needs, preferences, and values.
This remarkable transformation has le... |
131 | neurips2023_m3l | # Mathematics of Modern Machine Learning
Deep learning has demonstrated tremendous success in the past decade, sparking a revolution in artificial intelligence.
However, the modern practice of deep learning remains largely an art form, requiring a delicate combination of guesswork and careful hyperparameter tuning. Th... |
132 | neurips2023_mathai | # Mathematical Reasoning and AI
Mathematical reasoning is a fundamental aspect of human cognition that has been studied by scholars ranging from philosophers to cognitive scientists and neuroscientists. Mathematical reasoning involves analyzing complex information, identifying patterns and relationships, and drawing ... |
133 | neurips2023_med | # Medical Imaging
'Medical Imaging meets NeurIPS' is a satellite workshop established in 2017. The workshop aims to bring researchers together from the medical image computing and machine learning communities. The objective is to discuss the major challenges in the field and opportunities for joining forces. This year... |
134 | neurips2023_mlncp | # Machine Learning with New Compute Paradigms
Digital computing is approaching fundamental limits and faces serious challenges in terms of scalability, performance, and sustainability. At the same time, generative AI is fuelling an explosion in compute demand. There is, thus, a growing need to explore non-traditional ... |
135 | neurips2023_mlsys | # Overview
The ML for Systems workshop presents cutting-edge work on ML in computer systems and aims to develop a unified methodology for the field.
Machine Learning (ML) for Systems describes the application of machine learning techniques to problems related to computer systems. By leveraging supervised learning and... |
136 | neurips2023_mp2 | # Overview
The central theme of the workshop will be the application of moral philosophy and moral psychology theories to AI practices. Our invited speakers are some of the leaders in the emerging efforts to draw on theories in philosophy or psychology to develop ethical AI systems. Their talks will demonstrate cutting... |
137 | neurips2023_neurreps | # Workshop on Symmetry and Geometry in Neural Representations
An emerging set of findings in sensory and motor neuroscience is beginning to illuminate a new paradigm for understanding the neural code. Across sensory and motor regions of the brain, neural circuits are found to mirror the geometric and topological struc... |
138 | neurips2023_opt | # Optimization for Machine Learning
Optimization lies at the heart of many machine learning algorithms and enjoys great interest in our community. Indeed, this intimate relation of optimization with ML is the key motivation for the OPT series of workshops. We aim to foster discussion, discovery, and dissemination of s... |
139 | neurips2023_otml | # Optimal Transport and Machine Learning
Over the last decade, optimal transport (OT) has evolved from a prize-winning research area in pure mathematics to a recurring theme bursting across many areas of machine learning (ML). Advancements in OT theory, computation, and statistics have fueled breakthroughs in a wide r... |
140 | neurips2023_r0fomo | # R0-FoMo:Robustness of Few-shot and Zero-shot Learning in Large Foundation Models
Recent advances in the capabilities of large foundational models have been catalyzed by repurposing pretrained models to domain specific use cases through few-shot learning methods like prompt-tuning, in-context-learning; and zero-shot ... |
141 | neurips2023_realml | # Workshop on Adaptive Experimental Design and Active Learning in the Real World
This workshop aims to bring together researchers from academia and industry to discuss major challenges, outline recent advances, and highlight future directions pertaining to novel and existing real-world experimental design and active l... |
142 | neurips2023_regml | # Workshop on Regulatable ML
With the increasing deployment of machine learning in diverse applications affecting our daily lives, ethical and legal implications are rising to the forefront. Governments worldwide have responded by implementing regulatory policies to safeguard algorithmic decisions and data usage pract... |
143 | neurips2023_robotlearning | # Robot Learning Workshop: Pretraining, Fine-Tuning, and Generalization with Large Scale Models
Large pre-trained models have accelerated progress in many domains of machine learning research, such as text generation, chatbots, and image generation. In the 6th iteration of the Robot Learning workshop at NeurIPS, we wi... |
144 | neurips2023_ssltheorypractice | # Self-Supervised Learning - Theory and Practice
Self-supervised learning (SSL) is an unsupervised approach for representation learning without relying on human-provided labels. It creates auxiliary tasks on unlabeled input data and learns representations by solving these tasks. SSL has demonstrated great success on i... |
145 | neurips2023_syntheticdata4ml | # Workshop on Synthetic Data Generation with Generative AI
Advances in machine learning owe much to access to high quality training datasets and the well defined problem settings that they encapsulate. However, access to rich, diverse, and clean datasets may not always be possible. Moreover, three prominent issues: da... |
146 | neurips2023_tgl | # Temporal Graph Learning Workshop
Graphs are prevalent in many diverse applications including Social networks, Natural Language Processing, Computer Vision, the World Wide Web, Political Networks, Computational finance, Recommender Systems and more. Graph machine learning algorithms have been successfully applied to ... |
147 | neurips2023_trl | # Table Representation Learning Workshop
Tables are a promising modality for representation learning and generative models with too much application potential to ignore. However, tables have long been overlooked despite their dominant presence in the data landscape, e.g. data management and analysis pipelines. The maj... |
148 | neurips2023_unireps | # Unifying Representations in Neural Models
New findings in neuroscience and artificial intelligence reveal a shared pattern: whether in biological brains or artificial models, different learning systems tend to create similar representations when subject to similar stimuli.
The emergence of these similar representat... |
149 | neurips2023_want | # Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization
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 provi... |
150 | neurips2023_xaia | # ExplainableAI (XAI) in Action: Past, Present, and Future Applications
As AI models continue to advance in complexity and sophistication, understanding how they work and make decisions is becoming increasingly challenging. This challenge has prompted a surge of research into developing methods and tools that can enha... |
151 | neurips2024_advml_frontiers | ## New Frontiers in Adversarial Machine Learning
Adversarial machine learning (AdvML), a discipline that delves into the interaction of machine learning (ML) with ‘adversarial’ elements, has embarked on a new era propelled by the ever-expanding capabilities of artificial intelligence (AI). This momentum has been fueled... |
152 | neurips2024_afm | ## Adaptive Foundation Models: Evolving AI for Personalized and Efficient Learning
In the rapidly evolving landscape of AI, the development of adaptive foundation models represents a groundbreaking shift towards AI systems that can continually learn, adapt, and evolve in response to new information, changing environme... |
153 | neurips2024_ai4mat | ## AI for Accelerated Materials Design
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 ... |
154 | neurips2024_aidrugx | ## AI for New Drug Modalities
The primary objective of this workshop is to bridge the gap between AI and emerging drug modalities, such as gene, RNA, and cell therapies.
## Application Track
AI for DNA, RNA, and cell and gene therapeutics, which leverages cutting-edge AI methods. For example,
- AI for therapeutic ... |
155 | neurips2024_aim_fm | ## Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond
There have been notable advancements in large foundation models (FMs), which exhibit generalizable language understanding, visual recognition, and audio comprehension capabilities. These advancements highlight the potential ... |
156 | neurips2024_attrib | ## Attributing Model Behavior at Scale
Recently-developed algorithmic innovations and large-scale datasets have given rise to machine learning models with impressive capabilities. However, there is much left to understand in how these different factors combine to give rise to observed behaviors. For example, we still ... |
157 | neurips2024_audio_imagination | ## Audio Imagination: AI-Driven Speech, Music, and Sound Generation
Generative AI has been at the forefront of AI research in recent times, with numerous studies showcasing remarkable and surprising generation capabilities across various modalities such as text, image, and audio. Audio Imagination Workshop at NeurIPS ... |
158 | neurips2024_bdu | ## Workshop on Bayesian Decision-making and Uncertainty
Recent advances in ML and AI have led to impressive achievements, yet models often struggle to express uncertainty, and more importantly, make decisions that account for uncertainty. This hinders the deployment of AI models in critical applications, ranging from ... |
159 | neurips2024_behavioral_ml | ## Workshop on Behavioral Machine Learning
Across many application areas, machine learning systems rely on human data. Yet these systems often leave unmodelled the psychological processes that generate human data.
Fortunately, there's a field full of insights about human behavior: the behavioral sciences. However, ma... |
160 | neurips2024_calm | ## Causality and Large Models
The remarkable capabilities and accessibility of recent large models, also known as “foundation models,” have sparked significant interest and excitement in the research community and beyond. In particular, large pre-trained generative models have demonstrated remarkable competencies in un... |
161 | neurips2024_compositional_learning | ## Workshop on Compositional Learning: Perspectives, Methods, and Paths Forward
Compositional learning, inspired by the innate human ability to understand and generate complex ideas from simpler concepts, seek to imbue machines with a similar capacity for understanding, reasoning, and learning. Compositional learning ... |
162 | neurips2024_compression | ## Workshop on Machine Learning and Compression
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 exponential amount of data being g... |
163 | neurips2024_continual_fomo | ## Workshop on Scalable Continual Learning for Lifelong Foundation Models
For the pursuit of increasingly general intelligence, current foundation models are fundamentally limited by their training on static data, leading to outdated encoded information, saturation in knowledge accumulation, and wasteful use of comput... |
164 | neurips2024_crl | ## Causal Representation Learning Workshop
Advanced Artificial Intelligence (AI) techniques based on deep representations, such as GPT and Stable Diffusion, have demonstrated exceptional capabilities in analyzing vast amounts of data and generating coherent responses from unstructured data. They achieve this through s... |
165 | neurips2024_d3s3 | ## Workshop on Data-driven and Differentiable Simulations, Surrogates, and Solvers
Recent advances in Machine Learning highlights promising solutions to aid simulation-based scientific discovery e.g., regulating nuclear fusion, synthesizing new molecules, designing chips. Since ML-based techniques are inherently learn... |
166 | neurips2024_evaleval | ## Evaluating Evaluations: Examining Best Practices for Measuring Broader Impacts of Generative AI
Generative AI systems are becoming increasingly prevalent in society, producing text, images, audio, and video content with far-reaching implications. While the NeurIPS Broader Impact statement has notably shifted norms ... |
167 | neurips2024_federated_learning | ## Federated Foundation Models in Conjunction
Foundation models (FMs) are typically associated with large language models (LLMs), like ChatGPT, and are characterized by their scale and broad applicability. While these models provide transformative capabilities, they also introduce significant challenges, particularly ... |
168 | neurips2024_fitml | ## Workshop on Fine-Tuning in Modern Machine Learning: Principles and Scalability
This FITML workshop aims to contribute to the recent radical paradigm shift for fine-tuning in modern machine learning, theoretically, computationally, and systematically.
It encourages researchers to push forward the frontiers of theor... |
169 | neurips2024_fm4science | ## Foundation Models for Science: Progress, Opportunities, and Challenges
The integration of artificial intelligence (AI) and machine learning (ML) into the realm of science represents a pivotal shift in the traditional methods of scientific discovery. For centuries, the systematic and logical exploration of the natur... |
170 | neurips2024_fm_eduassess | ## Workshop on Large Foundation Models for Educational Assessment
The advanced generative artificial intelligence (AI) techniques, such as large language models and large multimodal models, are transforming many aspects of educational assessment. The integration of AI into education has the potential to revolutionize ... |
171 | neurips2024_genai4health | ## GenAI for Health: Potential, Trust and Policy Compliance
Generative AI (GenAI) emerged as a strong tool that can revolutionize healthcare and medicine. Yet the public trust in using GenAI for health is not well established due to its potential vulnerabilities and insufficient compliance with health policies. The wo... |
172 | neurips2024_imol | ## Intrinsically-Motivated and Open-Ended Learning
How do humans develop broad and flexible repertoires of knowledge and skills? How can we design autonomous lifelong learning machines with the same abilities?
A promising computational and scientific approach to these questions comes from the study of intrinsically m... |
173 | neurips2024_interpretableai | ## Interpretable AI: Past, Present and Future
Interpretability in machine learning revolves around constructing models that are inherently transparent and insightful for human end users. As the scale of machine learning models increases and the range of applications expands across diverse fields, the need for interpre... |
174 | neurips2024_langame | ## Language Gamification
Ludwig Wittgenstein, in his seminal work “Philosophical Investigations”, introduced the concept of “language games.” This framework views language as an adaptive system where words acquire meaning through use, emphasizing its social and interactive nature. Research in cognitive science reinfor... |
175 | neurips2024_m3l | ## Workshop on Mathematics of Modern Machine Learning
Deep learning has demonstrated tremendous success in the past decade, sparking a revolution in artificial intelligence.
However, the modern practice of deep learning remains largely an art form, requiring a delicate combination of guesswork and careful hyperparame... |
176 | neurips2024_math_ai | ## Workshop on Mathematical Reasoning and AI
Mathematical reasoning is a fundamental aspect of human cognition that has been studied by scholars ranging from philosophers to cognitive scientists and neuroscientists. Mathematical reasoning involves analyzing complex information, identifying patterns and relationships, ... |
177 | neurips2024_mint | ## MINT: Foundation Model Interventions
The increasing capabilities of foundation models have raised concerns about their potential to generate undesirable content, perpetuate biases, and promote harmful behaviors.
To address these issues, we are hosting a workshop at NeurIPS 2024 that focuses on understanding the i... |
178 | neurips2024_ml4ps | ## Machine Learning and the Physical Sciences Workshop
The Machine Learning and the Physical Sciences workshop aims to provide an informal, inclusive, and leading-edge venue for discussing research and challenges at the intersection of machine learning (ML) and the physical sciences (PS). This includes the application... |
179 | neurips2024_mlforsys | ## Machine Learning for Systems
Machine Learning for Systems is an interdisciplinary workshop that brings together researchers in computer systems and machine learning, specifically focusing on the novel application of machine learning techniques towards computer systems problems.
## Topics
We invite submission of u... |
180 | neurips2024_mlncp | ## Machine Learning with new Compute Paradigms
Digital computing is approaching fundamental limits and faces serious challenges in terms of scalability, performance, and sustainability. At the same time, generative AI is fuelling an explosion in compute demand. There is, thus, a growing need to explore non-traditional... |
181 | neurips2024_neuroai | ## NeuroAI
Welcome to the NeurIPS 2024 NeuroAI Workshop! This workshop aims to bring together researchers and practitioners from the fields of neuroscience and artificial intelligence.
We are in an era of unprecedented advancement in artificial intelligence, driven by the remarkable progress in artificial neural netw... |
182 | neurips2024_neurreps | ## Workshop on Symmetry and Geometry in Neural Representations
An emerging set of findings in sensory and motor neuroscience is beginning to illuminate a new paradigm for understanding the neural code. Across sensory and motor regions of the brain, neural circuits are found to mirror the geometric and topological stru... |
183 | neurips2024_opt | ## Optimization for Machine Learning
Optimization lies at the heart of many machine learning algorithms and enjoys great interest in our community. Indeed, this intimate relation of optimization with ML is the key motivation for the OPT series of workshops. We aim to foster discussion, discovery, and dissemination of ... |
184 | neurips2024_owa | ## Workshop on Open-World Agents
In recent years, AI has made significant strides in achieving success across various domains, demonstrating capabilities that often surpass human performance in specific tasks. However, the real world presents challenges that go beyond single tasks, objectives, or predefined, static env... |
185 | neurips2024_pluralistic_alignment | ## Pluralistic Alignment Workshop
Welcome to the Pluralistic Alignment Workshop! Aligning AI with human preferences and values is increasingly important. Yet, today’s AI alignment methods have been shown to be insufficient for capturing the vast space of complex – and often conflicting – real-world values. Our worksho... |
186 | neurips2024_rbfm | ## Workshop on Responsibly Building the Next Generation of Multimodal Foundational Models
In recent years, the importance of interdisciplinary approaches focusing on multimodality (language+image+video+audio) has grown exponentially, driven by their impact in fields such as robotics. However, the rapid evolution of the... |
187 | neurips2024_red_teaming_genai | ## Red Teaming GenAI: What Can We Learn from Adversaries?
With the rapid development of Generative AI, ensuring their safety, security, and trustworthiness is paramount. In response, researchers and practitioners have proposed red teaming to identify such risks, enabling their mitigation. Red teaming refers to adversa... |
188 | neurips2024_regml | ## Workshop on Regulatable ML
With the increasing deployment of machine learning in diverse applications affecting our daily lives, ethical and legal implications are rising to the forefront. Governments worldwide have responded by implementing regulatory policies to safeguard algorithmic decisions and data usage pra... |
189 | neurips2024_safegenai | ## Safe Generative AI Workshop
In recent years, many AI researchers believe that advanced AI systems could potentially put human society at risk, especially if these systems become smarter than humans. Generative models have been the major driving force behind the development of advanced AI in the past two years. This... |
190 | neurips2024_sata | ## Workshop on Safe & Trustworthy Agents
This workshop aims to clarify key questions on the safety of agentic AI systems and foster a community of researchers working in this area.
## Topics
This workshop aims to clarify key questions on the trustworthiness of agentic AI systems and foster a community of researcher... |
191 | neurips2024_scifordl | ## Workshop on Scientific Methods for Understanding Deep Learning
While deep learning continues to achieve impressive results on an ever-growing range of tasks, our understanding of the principles underlying these successes remains largely limited. This problem is usually tackled from a mathematical point of view, aim... |
192 | neurips2024_sfllm | ## Statistical Foundations of LLMs and Foundation Models
Statistics has historically been the tool of choice for understanding and mitigating the operational risks of engineering deployments.
We need new statistical tools for the era of black-box models where the standard statistical ideas don't apply.
## Topics
Doe... |
193 | neurips2024_solar | ## Workshop on Socially Responsible Language Modelling Research
The Socially Responsible Language Modelling Research (SoLaR) workshop at NeurIPS 2024 is an interdisciplinary gathering that aims to foster responsible and ethical research in the field of language modeling. Recognizing the significant risks and harms ass... |
194 | neurips2024_ssl | ## Self-Supervised Learning - Theory and Practice
Self-supervised learning (SSL) is an approach of representation learning that does not rely on human-labeled data. Instead, it creates auxiliary tasks from unlabeled input data and learns representations by solving these tasks. SSL has shown significant success across ... |
195 | neurips2024_sys2_reasoning | ## Workshop on System-2 Reasoning at Scale
System 2 Reasoning At Scale focuses on improving reasoning in neural networks, particularly the challenges and strategies for achieving System-2 reasoning in transformer-like models. The workshop addresses issues like distinguishing memorization from rule-based learning, unde... |
196 | neurips2024_trl | ## Table Representation Learning Workshop
Tables are a promising modality for representation learning and generative models with too much application potential to ignore. However, tables have long been overlooked despite their dominant presence in the data landscape, e.g. data management and analysis pipelines. The ma... |
197 | neurips2024_tsalm | ## Workshop on Time Series in the Age of Large Models
Foundation models have revolutionized the approach to building machine learning models in areas like natural language processing, where models are pretrained on large amounts of diverse data and then adapted for downstreams tasks, often in a zero-shot fashion. This ... |
198 | neurips2024_unireps | # Workshop on Unifying Representations in Neural Models
### When, how and why do different neural models learn the same representations?
New findings in neuroscience and artificial intelligence reveal a shared pattern: whether in biological brains or artificial models, different learning systems tend to create simila... |
199 | neurips2024_video_language_models | ## Workshop on Video-Language Models
Touch is a crucial sensor modality for both humans and robots, as it allows us to directly sense object properties and interactions with the environment. Recently, touch sensing has become more prevalent in robotic systems, thanks to the increased accessibility of inexpensive, rel... |
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