Theory
1,949 items in 14 lists
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Leveraging Facial Expressions and Contextual Information to Investigate Opaque Representations of Emotion psycnet.apa.org
Emotion, 2019. [All Versions].
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Tool use and affordance: Manipulation-based versus reasoning-based approaches psycnet.apa.org
Psychological Review, 2016. [All Versions]. A classic review on human tool use and affordance.
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Word learning as Bayesian inference psycnet.apa.org
Psychological Review, 2007. [All Versions]. [Preprint]. The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a…
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Causal Reasoning in Rats psych.uni-goettingen.de
Science, 2006. [All Versions]. A piece of evidence for the capability of causal reasoning in intelligent animals.
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Predictive and Diagnostic Learning Within Causal Models: Asymmetries in Cue Competition psych.uni-goettingen.de
Journal of Experimental Psychology, 1992. [All Versions]. Experimental evidences for distincting causality and association.
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Intrinsic Exploration as Empowerment in a Richly Structured Online Game psyarxiv.com
2022. [All Versions].
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Partial Mental Simulation Explains Fallacies in Physical Reasoning psyarxiv.com
Cognitive Neuropsychology, 2022. [All Versions].
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The Signature of All Things: Children Infer Knowledge States from Static Images psyarxiv.com
CogSci'20, 2020. [All Versions].
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Nonparametric Bayesian Data Analysis projecteuclid.org
Statistical Science, 2004. [All Versions]. This paper reviews the current state of nonparametric Bayesian inference. The discussion follows a list of important statistical inference problems, including density estimation, regression, survival analysis, hierarchical models and…
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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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Enhancing Robot Program Synthesis Through Environmental Context proceedings.neurips.cc
NeurIPS'23, 2023. [All Versions]. Program synthesis aims to automatically generate an executable program that conforms to the given specification. Recent advancements have demonstrated that deep neural methodologies and large-scale pretrained language models are highly…
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Emergent Graphical Conventions in a Visual Communication Game proceedings.neurips.cc
NeurIPS, 2022. [All Versions]. A computational account on the emergence of iconic language.
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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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Fast Abductive Learning by Similarity-based Consistency Optimization proceedings.neurips.cc
NeurIPS'21, 2021. [All Versions]. An approach for accelerating the convergence of Abductive Learning.
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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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Compositional Explanations of Neurons proceedings.neurips.cc
NeurIPS'20, 2020. [All Versions]. [Project]. A concept-composition version of network dissection.
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Learning Compositional Rules via Neural Program Synthesis proceedings.neurips.cc
NeurIPS'20, 2020. [All Versions].
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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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Learning to Infer Graphics Programs from Hand-Drawn Images proceedings.neurips.cc
NeurIPS'18, 2018. [All Versions]. The method learns a model that uses program synthesis techniques to recover a graphics program from drawing primitives. These programs have constructs like variable bindings, iterative loops, or simple kinds of conditionals. With a graphics…
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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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VISALOGY: Answering Visual Analogy Questions proceedings.neurips.cc
NeurIPS'15, 2015. [All Versions].
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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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Testing a Bayesian Measure of Representativeness Using a Large Image Database proceedings.neurips.cc
NeurIPS'11, 2011. [All Versions].
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Efficient Learning of Sparse Representations with an Energy-Based Model proceedings.neurips.cc
NeurIPS'06, 2006. [All Versions].
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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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From Algorithmic to Subjective Randomness proceedings.neurips.cc
NeurIPS'03, 2003. [All Versions].
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Hierarchical topic models and the nested Chinese restaurant process proceedings.neurips.cc
NeurIPS'03, 2003. [All Versions]. The original paper for nested Chinese restaurant process.
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Rules and Similarity in Concept Learning proceedings.neurips.cc
NeurIPS'99, 1999. [All Versions]. [Preprint]. This paper argues that two apparently distinct modes of generalizing concepts - abstracting rules and computing similarity to exemplars - should both be seen as special cases of a more general Bayesian learning framework. Bayes…
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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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Analogies Explained: Towards Understanding Word Embeddings proceedings.mlr.press
ICML'19, 2019. [All Versions]. Explaining the analogy capability in word embeddings.
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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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Curiosity-driven Exploration by Self-supervised Prediction proceedings.mlr.press
ICML'17, 2017. [All Versions]. The original paper on curiosity as intrinsic motivation.
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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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A Theory of Generative ConvNet proceedings.mlr.press
ICML'16, 2016. [All Versions]. The authors show that a generative random field model, which they call generative ConvNet, can be derived from the commonly used discriminative ConvNet, by assuming a ConvNet for multi-category classification and assuming one of the category is a…
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Trust Region Policy Optimization proceedings.mlr.press
ICML'15, 2015. [All Versions]. The original paper introducing TRPO, a method for optimizing control policies, with guaranteed monotonic improvement.
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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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