AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks proceedings.mlr.press
ICML'17, 2017. [All Versions]. [Post]. Chelsea Finn's original paper on Model-Agnostic Meta-Learning (MAML).
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Constrained Policy Optimization proceedings.mlr.press
ICML'17, 2017. [All Versions]. The original paper on constrained reinforcement learning (safe reinforcement learning).
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Analogy-preserving Semantic Embedding for Visual Object Categorization proceedings.mlr.press
ICML'13, 2013. [All Versions]. The first application of analogy to machine learning.
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On the Complexity of Bayesian Generalization proceedings.mlr.press
ICML'23, 2023. [All Versions]. [Project]. [Models]. This work examines concept generalization at a large scale in the natural visual spectrum. Established computational modes (i.e., rule-based or similarity-based) are primarily studied isolated, focusing on confined and…
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PAL: Program-aided Language Models proceedings.mlr.press
ICML'23, 2023. [All Versions]. Paper presenting an approach that uses the LLM to read natural language problems and generate programs as the intermediate reasoning steps, but offloads the solution step to a runtime such as a Python interpreter. With PAL, decomposing the natural…
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AGENT: A Benchmark for Core Psychological Reasoning proceedings.mlr.press
ICML'21, 2021. [All Versions]. Inspired by cognitive development studies on intuitive psychology, this paper presents a benchmark consisting of a large dataset of procedurally generated 3D animations, AGENT (Action, Goal, Efficiency, coNstraint, uTility), structured around four…
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TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL proceedings.mlr.press
ICML'21, 2021. [All Versions]. [Project].
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Inverse Constrained Reinforcement Learning proceedings.mlr.press
ICML'21, 2021. [All Versions].
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SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning proceedings.mlr.press
ICML'21, 2021. [All Versions]. [Code].
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Bayesian modeling of human–AI complementarity pnas.org
Proceedings of the National Academy of Sciences, 2022. [All Versions]. A Bayesian framework for combining the predictions and different types of confidence scores from humans and machines.
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Understanding the role of Individual Units in a Deep Neural Network pnas.org
Proceedings of the National Academy of Sciences, 2020. [All Versions]. David Bau's review on network dissection for discriminative and generative models.
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Rapid trail-and-error learning with simulation supports flexible tool use and physical reasoning. pnas.org
Proceedings of the National Academy of Sciences, 2020. [All Versions]. [Project]. [Appendix]. Many animals, and an increasing number of artificial agents, display sophisticated capabilities to perceive and manipulate objects. But human beings remain distinctive in their…
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Emergence of analogy from relation learning pnas.org
Proceedings of the National Academy of Sciences, 2019. [All Versions]. Analogy feature in language models.
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Communicating artificial neural networks develop efficient color-naming systems pnas.org
Proceedings of the National Academy of Sciences, 2021. [All Versions]. Simulating the emergence of code as the communication bottleneck in color learning task.
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Computational evidence for hierarchically structured reinforcement learning in humans pnas.org
Proceedings of the National Academy of Sciences, 2020. [All Versions]. A piece of evidence on hierarchical human planning.
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Acquiring Comparative Commonsense Knowledge from the Web people.mpi-inf.mpg.de
AAAI'14, 2014. [All Versions].
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This Looks Like That: Deep Learning for Interpretable Image Recognition papers.neurips.cc
NeurIPS'19, 2019. [All Versions].
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Bridging Machine Learning and Logical Reasoning by Abductive Learning. papers.neurips.cc
NeurIPS'19, 2019. [All Versions]. [Slides]. [Code]. The original paper on Abductive Learning, a derivative-free approach for neuro-symbolic learning.
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On the Expressivity of Markov Reward papers.neurips.cc
NeurIPS'21, 2021. [All Versions]. A formal treatment of tasks and rewards in reinforcement learning modeling.
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Visual Concept-Metaconcept Learning papers.neurips.cc
NeurIPS'19, 2019. [All Versions].
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On Monte Carlo Tree Search and Reinforcement Learning oucsace.cs.ohio.edu
Journal of Artificial Intelligence Research, 2017. [All Versions].
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Learn to explain efficiently via neural logic inductive learning openreview.net
ICLR'20, 2020. [All Versions]. [Project].
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Abductive Commonsense Reasoning openreview.net
ICLR'20, 2020. [All Versions]. Abductive commonsense reasoning on large language models.
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Program Guided Agent openreview.net
ICLR'20, 2020. [All Versions].
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Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration openreview.net
ICLR'21, 2021. [All Versions].
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Learning to Perform Physics Experiments via Deep Reinforcement Learning openreview.net
ICLR'17, 2017. [All Versions].
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NBDT: Neural-Backed Decision Trees openreview.net
ICLR'21, 2021. [All Versions]. [Code]. Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models…
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HLSMAC: A New StarCraft Multi-Agent Challenge for High-Level Strategic Decision-Making openreview.net
AAMAS'26, 2026. [All Versions]. Benchmarks are crucial for assessing multi-agent reinforcement learning (MARL) algorithms. While StarCraft II-related environments have driven significant advances in MARL, existing benchmarks like SMAC focus primarily on micromanagement…
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Meta-Q-Learning openreview.net
ICLR'20, 2020. [All Versions]. The milestone paper on context Meta-RL.
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Physion: Evaluating Physical Prediction from Vision in Humans and Machines openreview.net
NeurIPS'21, 2021. [All Versions].
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Metacontrol for Adaptive Imagination-Based Optimization openreview.net
ICLR'17, 2017. [All Versions].
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Hierarchically Encapsulated Representation for Protocol Design in Self-Driving Labs openreview.net
ICLR'25, 2025. [All Versions]. [Project]. Self-driving laboratories have begun to replace human experimenters in performing single experimental skills or predetermined experimental protocols. However, as the pace of idea iteration in scientific research has been intensified by…
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Solving Compositional Reinforcement Learning Problems via Task Reduction openreview.net
ICLR'21, 2021. [All Versions].
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Visual Programming: Compositional Visual Reasoning Without Training openaccess.thecvf.com
CVPR'23, 2023. [All Versions]. VISPROG, a neuro-symbolic approach to solving complex and compositional visual tasks given natural language instructions, using the in-context learning ability of large language models to generate python-like modular programs, which are then…
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Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic onlinelibrary.wiley.com
Topics in Cognitive Science, 2015. [All Versions]. An earlier version of the paper above.
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The AHA! Experience: Creativity Through Emergent Binding in Neural Networks onlinelibrary.wiley.com
Cognitive Science, 2012. [All Versions].
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What Is the Model in Model-Based Planning? onlinelibrary.wiley.com
Cognitive Science, 2021. [All Versions].
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PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception ojs.aaai.org
AAAI'21, 2021. [All Versions]. [Project].
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Theory-Based Causal Transfer: Integrating Instance-Level Induction and Abstract-Level Structure Learning ojs.aaai.org
AAAI'20, 2020. [All Versions]. A computatinoal account on causal transfer.
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Abduction-Based Explanations for Machine Learning Models ojs.aaai.org
AAAI'19, 2019. [All Versions]. The growing range of applications of Machine Learning (ML) in a multitude of settings motivates the ability of computing small explanations for predictions made. Small explanations are generally accepted as easier for human decision makers to…
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Quantifying artificial intelligence through algorithmic generalization nature.com
Nature Machine Intelligence, 2025. [All Versions]. The rapid development of artificial intelligence (AI) systems has created an urgent need for their scientific quantification. While their fluency across a variety of domains is impressive, AI systems fall short on tests…
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The future of open human feedback nature.com
Nature Machine Intelligence, 2025. [All Versions]. Human feedback on conversations with language models is central to how these systems learn about the world, improve their capabilities and are steered towards desirable and safe behaviours. However, this feedback is mostly…
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Embodied large language models enable robots to complete complex tasks in unpredictable environments nature.com
Nature Machine Intelligence, 2025. [All Versions]. Completing complex tasks in unpredictable settings challenges robotic systems, requiring a step change in machine intelligence. Sensorimotor abilities are considered integral to human intelligence. Thus, biologically inspired…
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Large language models for scientific discovery in molecular property prediction nature.com
Nature Machine Intelligence, 2025. [All Versions]. Large language models (LLMs) are a form of artificial intelligence system encapsulating vast knowledge in the form of natural language. These systems are adept at numerous complex tasks including creative writing, storytelling…
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Augmenting large language models with chemistry tools nature.com
Nature Machine Intelligence, 2023. [All Versions]. [Preprint]. This paper introduces ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery and materials design. By integrating 18 expert-designed tools and using GPT-4 as the LLM…
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Explaining machine learning models with interactive natural language conversations using TalkToModel nature.com
Nature Machine Intelligence, 2023. [All Versions]. Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions…
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Phy-Q as a measure for physical reasoning intelligence nature.com
Nature Machine Intelligence, 2023. [NMI Challenge]. An interactive benchmark for AI physical reasoning.
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An intelligent guided troubleshooting method for aircraft based on HybirdRAG nature.com
Scientific Reports, 2025. [All Versions]. To enhance aircraft fault diagnosis efficiency, this paper proposes HybridRAG, an intelligent-guided troubleshooting framework that integrates knowledge graphs and large language models (LLMs). Unlike conventional retrieval-augmented…
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Dual retrieving and ranking medical large language model with retrieval augmented generation nature.com
Scientific Reports, 2025. [All Versions]. Recent advancements in large language models (LLMs) have significantly enhanced text generation across various sectors; however, their medical application faces critical challenges regarding both accuracy and real-time responsiveness…
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Meta-analysis of the functional neuroimaging literature with probabilistic logic programming nature.com
Scientific Reports, 2022. [All Versions]. Inferring reliable brain-behavior associations requires synthesizing evidence from thousands of functional neuroimaging studies through meta-analysis. However, existing meta-analysis tools are limited to investigating simple…
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