AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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Metacognition in computation: A selected research review sciencedirect.com
Artificial Intelligence, 2005. [All Versions].
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Qualitative choice logic sciencedirect.com
Artificial Intelligence, 2004. [All Versions].
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Learning physical parameters from dynamic scenes sciencedirect.com
Cognitive Psychology, 2017. [All Versions].
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Probabilistic Horn abduction and Bayesian networks sciencedirect.com
Artificial Intelligence, 1993. [All Versions]. This paper presents a simple framework for Horn-clause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is…
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Interpretation as abduction sciencedirect.com
Artificial Intelligence, 1993. [All Versions]. Abduction is inference to the best explanation. The authors have developed an approach to abductive inference, called “weighted abduction”, that has resulted in a significant simplification of how the problem of interpreting texts…
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The structure-mapping engine: Algorithm and examples sciencedirect.com
Artificial Intelligence, 1989. [All Versions]. A computational implementation of analogy.
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SOAR: An architecture for general intelligence sciencedirect.com
Artificial Intelligence, 1987. [All Versions].
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Qualitative Simulation sciencedirect.com
Artificial Intelligence, 1986. [All Versions]. [Preprint]. This paper presents a precise definition of qualitative structure and behavior descriptions as abstractions of differential equations and continuously differentiable functions. The authors present a new algorithm for…
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Commonsense reasoning about causality: Deriving behavior from structure sciencedirect.com
Artificial Intelligence, 1984. [All Versions].
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In situ bidirectional human-robot value alignment science.org
Science Robotics, 2022. [All Versions]. [Preprint]. This paper proposes an explainable artificial intelligence (XAI) system in which a group of robots predicts users’ values by taking in situ feedback into consideration while communicating their decision processes to users…
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A principal odor map unifies diverse tasks in olfactory perception science.org
Science, 2023. [All Versions]. [Code]. [Data (Reproduced)]. [Preprint]. [GoodScents Database]. [Leffingwell Database]. Mapping molecular structure to odor perception is a key challenge in olfaction. This work used graph neural networks to generate a principal odor map (POM)…
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A universal system for digitization and automatic execution of the chemical synthesis literature science.org
Science, 2020. [All Versions]. [Preprint]. [XDL Documentation]. [XDL Schema Database]. This paper reports a software platform that uses natural language processing to translate the organic chemistry literature directly into editable code, which in turn can be compiled to drive…
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A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs science.org
Science, 2017. [All Versions]. [Preprint]. Learning from a few examples and generalizing to markedly different situations are capabilities of human visual intelligence that are yet to be matched by leading machine learning models. By drawing inspiration from systems…
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Human-level concept learning through probabilistic program induction science.org
Science, 2015. [All Versions]. [Preprint]. [Supplementary Material]. People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy…
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How to Grow a Mind: Statistics, Structure, and Abstraction science.org
Science, 2011. [All Versions]. [Preprint]. This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more humanlike machine learning systems. Computational models that perform probabilistic inference…
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Science Advances, 2019. [All Versions]. In the process of finding high-performance materials for organic photovoltaics (OPVs), it is meaningful if one can establish the relationship between chemical structures and photovoltaic properties even before synthesizing them. This work…
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AI Feynman: A physics-inspired method for symbolic regression science.org
Science Advances, 2019. [All Versions]. A core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of…
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Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs science.org
Science Robotics, 2019. [All Versions]. Humans can infer concepts from image pairs and apply those in the physical world in a completely different setting, enabling tasks like IKEA assembly from diagrams. If robots could represent and infer high-level concepts, then it would…
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A tale of two explanations: Enhancing human trust by explaining robot behavior science.org
Science Robotics, 2019. [All Versions]. [Preprint]. The ability to provide comprehensive explanations of chosen actions is a hallmark of intelligence. Lack of this ability impedes the general acceptance of AI and robot systems in critical tasks. This paper examines what forms…
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Tool use as adaptation royalsocietypublishing.org
Philosophical Transactions of the Royal Society B: Biological Sciences, 2013. [All Versions].
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Robotic hand augmentation drives changes in neural body representation robotics.sciencemag.org
Science Robotics, 2021. [All Versions].
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Deep Learning and the Information Bottleneck Principle robotics.caltech.edu
IEEE Information Theory Workshop'15, 2015. [All Versions]. The first paper identifying the problem of information bottleneck in representation learning.
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How variability shapes learning and generalization pure.mpg.de
Trends in Cognitive Sciences, 2022. [All Versions]. A comprehensive review on the trade-off between variability and generalization ability.
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Automation isn't automatic pubs.rsc.org
Chemical Science, 2021. [All Versions]. This perspective provides an overview of the current state of automation of synthetic chemistry at the benchtop scale with a particular emphasis on core considerations and the ensuing challenges of deploying a system. The authors aim to…
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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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