AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge pubs.acs.org
Journal of the American Chemical Society, 2024. [All Versions]. The development of AI synthesis planners trained solely on reaction-example-data has stagnated and is not on par with the performance of “hybrid” algorithms combining AI with expert knowledge. This Perspective…
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A theory of relation learning and cross-domain generalization psycnet.apa.org
Psychological Review, 2022. [All Versions]. A comprehensive review on the perspective of treating analogy as cross-domain generalization.
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The language of generalization psycnet.apa.org
Psychological Review, 2019. [All Versions].
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Hypothesis generation, sparse categories, and the positive test strategy psycnet.apa.org
Psychological Review, 2011. [All Versions].
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Tools, language and cognition in human evolution psycnet.apa.org
Cambridge University Press, 1993. [All Versions]. A classic perspective correlating human tool use with the evolution of civilization.
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Expert-level protocol translation for self-driving labs proceedings.neurips.cc
NeurIPS'24, 2024. [All Versions]. [Project]. Recent development in Artificial Intelligence (AI) models has propelled their application in scientific discovery, but the validation and exploration of these discoveries require subsequent empirical experimentation. The concept of…
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Grammar Prompting for Domain-Specific Language Generation with Large Language Models proceedings.neurips.cc
NeurIPS'23, 2023. [All Versions]. Grammar prompting is a simple approach to enable LLMs to use external knowledge and domain-specific constraints expressed through a grammar in Backus--Naur Form (BNF) during in-context learning. Grammar prompting augments each demonstration…
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Communicating Natural Programs to Humans and Machines proceedings.neurips.cc
NeurIPS'22, 2022. [All Versions]. While humans readily generate and interpret instructions in a general language, computer systems are shackled to a narrow domain-specific language that they can precisely execute. This makes building intelligent systems that can generalize to…
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When to Trust Your Model: Model-Based Policy Optimization proceedings.neurips.cc
NeurIPS'19, 2019. [All Versions]. [Post].
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Bayesian modeling of human concept learning proceedings.neurips.cc
NeurIPS'98, 1998. [All Versions]. [Preprint]. This work considers the problem of learning concepts from small numbers of positive examples, a feat which humans perform routinely but which computers are rarely capable of. Bridging machine learning and cognitive science…
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Program Synthesis Guided Reinforcement Learning proceedings.neurips.cc
NeurIPS'21, 2021. [All Versions].
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On Effective Scheduling of Model-based Reinforcement Learning proceedings.neurips.cc
NeurIPS'21, 2021. [All Versions].
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Learning Latent Space Energy-Based Prior Model proceedings.neurips.cc
NeurIPS'20, 2020. [All Versions]. [Project]. [Code]. A milestone paper on Latent Energy-Based Model.
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Understanding Deep Architectures with Reasoning Layer proceedings.neurips.cc
NeurIPS'20, 2020. [All Versions].
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A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation proceedings.neurips.cc
NeurIPS'19, 2019. [All Versions].
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PHYRE: A New Benchmark for Physical Reasoning proceedings.neurips.cc
NeurIPS'19, 2019. [All Versions]. A benchmark for AI physical reasoning.
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Learning by Abstraction: The Neural State Machine proceedings.neurips.cc
NeurIPS'19, 2019. [All Versions].
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Bayesian Model-Agnostic Meta-Learning proceedings.neurips.cc
NeurIPS'18, 2018. [All Versions]. A Bayesian account on MAML.
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Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same… proceedings.neurips.cc
NeurIPS'18, 2018. [All Versions]. Maching the learned pattern of neurons in different neural networks.
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Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols proceedings.neurips.cc
NeurIPS'18, 2018. [All Versions].
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Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning proceedings.neurips.cc
NeurIPS'15, 2015. [All Versions]. The original paper on empowerment as intrinsic motivation.
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Practical Bayesian Optimization of Machine Learning Algorithms proceedings.neurips.cc
NeurIPS'12, 2012. [All Versions]. The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a “black art” requiring expert experience, rules of thumb, or sometimes brute-force…
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Intrinsically Motivated Reinforcement Learning proceedings.neurips.cc
NeurIPS'04, 2004. [All Versions]. A comprehensive review on intrinsic reward functions in classic reinforcement learning.
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Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables proceedings.mlr.press
ICML'19, 2019. [All Versions].
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Machine theory of mind proceedings.mlr.press
ICML'18, 2018. [All Versions]. Theory of mind (ToM) broadly refers to humans’ ability to represent the mental states of others, including their desires, beliefs, and intentions. This work proposes a Theory of Mind neural network --- a ToMnet --- which uses meta-learning to…
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Investigating Human Priors for Playing Video Games proceedings.mlr.press
ICML'18, 2018. [All Versions].
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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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