Top 50 Awesome List

Higher Education

Higher Education

YuzheSHI/awesome-agi-cocosci

Theory  4 days ago  79
An awesome & curated list for Artificial General Intelligence, an emerging inter-discipline field that combines artificial intelligence and computational cognitive sciences.
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Aug 12th

Institute & Researcher

Others

  • Mark Ho - Department of Computer Science, Stevens Institute of Technology.
  • Aug 10th

    Papers

    Meta Learning

  • Meta-Q-Learning - ICLR'20, 2020. [All Versions]. The milestone paper on context Meta-RL.
  • Aug 7th

    Academic Tools

    Courses

  • Structure and Interpretation of Computer Programs - MIT. [Book: SICP]. Classic course on applying structural, procedural, and meta-linguistic abstraction to solve computational problems.
  • Papers

    Knowledge Representation

  • Concepts in a Probabilistic Language of Thought - Center for Brains, Minds, and Machines MEMO No.010, 2014. [All Versions].
  • Aug 4th

    Papers

    Virtual Reality

  • How Immersive Is Enough? A Meta-Analysis of the Effect of Immersive Technology on User Presence - Media Psychology, 2016. [All Versions]. A meta-analysis on the extent to which technologies need to be immersive in order to generate a sense of presence.
  • Aug 1st

    Papers

    Quantitative Analysis

  • With or Without U? The Appropriate Test for a U-Shaped Relationship - Oxford Bulletin of Economics and Statistics, 2010. [All Versions]. The original method for testing U-shape relation from the data, which is distinctive from the quadratic regression test.
  • Jul 27th

    Academic Tools

    Paper Writing

  • LaTex Configuration - LaTex. LaTex template for configuration file with elegant reference style (gray-colored reference, page backward reference).
  • BibTex Template - BibTex. BibTex template for including abbreviations of journals and conferences in AI, Mathematics, and Cognitive Sciences.
  • Papers

    Causality

  • Reasoning with cause and effect - 1998. Judea Pearl's tutorials on causal reasoning with operations on Bayesian networks.
  • Institute & Researcher

    Stanford

  • Li Fei-Fei - Computer Science Department, Human-Centered AI Institute, Stanford, Stanford Vision and Learning Lab - Stanford.
  • Noah Goodman - Stanford, Computation & Cognition Lab (CoCoLab) - Department of Psychology, Computer Science Department, Stanford.
  • Chelsea Finn - Stanford, Intelligence through Robotic Interaction at Scale (IRIS Group) - Computer Science Department, Stanford.
  • Jiajun Wu - Computer Science Department, Stanford.
  • Academic Tools

    Knowledge Management

  • Zotero - Digital Scholar. For reference management to manage bibliographic data and research related materials.
  • Institute & Researcher

    Harvard

  • Fiery Cushman - Department of Psychology, Harvard, Moral Psychology Research Lab - Harvard.
  • Jul 26th

    Institute & Researcher

    Princeton

  • Thomas Griffiths - Princeton, Computational Cognitive Science Lab - Department of Psychology, Department of Computer Science, Princeton.
  • Jul 24th

    Institute & Researcher

    Others

  • Yixin Zhu - School of AI, Institute for AI, Peking University (PKU).
  • Chaz Firestone - Department of Psychological and Brain Sciences, John Hopkins University (JHU), Hopkins Perception & Mind Lab - JHU.
  • Jul 23rd

    Papers

    Language Compositionality

  • Word formation supports efficient communication: The case of compounds - CogSci'22, 2022.
  • Papers

    Human-Level Problem Solving

  • Learning to act by integrating mental simulations and physical experiments - CogSci'18, 2018. [All Versions]. [Codestars1].
  • Papers

    Planning

  • Discovering State and Action Abstractions for Generalized Task and Motion Planning - AAAI'22, 2022. [All Versions].
  • Papers

    Quantitative Analysis

  • Identification of Causal Effects Using Instrumental Variables - Journal of the American Statistical Association, 1996. [All Versions]. The original paper on Instrumental Variables for natural sociology studies.
  • Experiments with More Than One Random Factor: Designs, Analytic Models, and Statistical Power - Annual Review of Psychology, 2017. [All Versions]. A comprehensive review of the quantitative analysis techniques for behavioral studies.
  • Papers

    Marr Levels of Analysis

  • Vision: A Computational Investigation into the Human Representation and Processing of Visual Information - MIT Press, 1982. [All Versions]. David Marr's original book on the levels of analysis.
  • Bridging Levels of Analysis for Probabilistic Models of Cognition - Current Directions in Psychological Science, 2012. [All Versions]. A Marr's paradigm account on probabilistic models.
  • Levels of Analysis in Computational Social Science - CogSci'18, 2018. [All Versions]. A Marr's paradigm account on computational social science.
  • Levels of Analysis for Machine Learning - ICLR'20 Bridging AI and Cognitive Science Workshop, 2020. [All Versions]. A Marr's paradigm account on machine learning.
  • Papers

    Causality

  • Identification of Causal Effects Using Instrumental Variables - Journal of the American Statistical Association, 1996. [All Versions].
  • Causality - Wikipedia. Wikipedia on causality, which is influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause.
  • Causal Models - Plato Stanford. A computational philosophy account on Causal models, which are mathematical models representing causal relationships within an individual system or population.
  • Causal Theories of Mental Content - Plato Stanford. A computational philosophy account on causal theories of mental content, which attempts to explain how thoughts can be about things.
  • The Seven Tools of Causal Inference, with Reflections on Machine Learning - Communications of the ACM, 2019. [All Versions]. Judea Pearl's review on causal inference in probabilistic graph models.
  • Toward Causal Representation Learning - Proceedings of the IEEE, 2021. [All Versions]. Yoshua Bengio's review on the perspective of treating causal inference as a representation learning problem.
  • Theory-Based Causal Induction - Psychological Review, 2009. [All Versions]. Thomas Griffiths' review on causal Bayesian theory induction.
  • Theory-Based Causal Transfer: Integrating Instance-Level Induction and Abstract-Level Structure Learning - AAAI'20, 2020. [All Versions]. A computatinoal account on causal transfer.
  • Causal Reasoning in Rats - Science, 2006. [All Versions]. A piece of evidence for the capability of causal reasoning in intelligent animals.
  • Do New Caledonian crows solve physical problems through causal reasoning? - Proceedings of the Royal Society B: Biological Sciences, 2009. [All Versions]. A piece of evidence for the capability of causal reasoning in intelligent animals.
  • People & Book

    David Marr

  • Vision: A Computational Investigation into the Human Representation and Processing of Visual Information - MIT Press, 1982. [All Versions].
  • Papers

    Embodied Intelligence

  • Embodied Cognition - Plato Stanford. A computational philosophy account on Embodied Cognition, which emphasizes the significance of an agent's physical body in cognitive abilities.
  • Cognitive engineering: Human problem solving with tools - Human Factors, 1988. [All Versions]. The original idea of investigating huamn tool use in problem solving.
  • Tools, language and cognition in human evolution - Cambridge University Press, 1993. [All Versions]. A classic perspective correlating human tool use with the evolution of civilization.
  • The Extended Mind - Analysis, 1998. [All Versions]. The original paper on the debate of mind externalism.
  • The neural bases of complex tool use in humans - Trends in Cognitive Sciences, 2004. [All Versions]. A neuroscience account of human tool use.
  • Spontaneous Metatool Use by New Caledonian Crows - Current Biology, 2007. [All Versions]. A piece of evidence that intelligent animals can take advantage of matatools to make tools for problem solving.
  • Tool use and affordance: Manipulation-based versus reasoning-based approaches - Psychological Review, 2016. [All Versions]. A classic review on human tool use and affordance.
  • Understanding Tools: Task-Oriented Object Modeling, Learning and Recognition - CVPR'15, 2015. [All Versions]. [Project]. The original paper introducing affordance and physically-grounded tool use into computer vision.
  • Externalism About the Mind - Plato Stanford. A computational philosophy account on mind externalism, a long-term debate about the boundary of embodied intelligence.
  • Papers

    Cognitive Architecture

  • The secret life of predictive brains: what's spontaneous activity for? - Trends in Cognitive Sciences, 2021. [All Versions]. A neuroscience account on brain as a generative model.
  • What is consciousness, and could machines have it? - Science, 2017. [All Versions]. A perspective on the two levels of consciousness in machine intelligence.
  • Is human cognition adaptive? - Behavioral and Brain Sciences, 1991. [All Versions]. The original paper introducing the adaptation perspective of human intelligence, the theoretical basis of the ACT cognitive architecture.
  • Papers

    Inductive Logic & Program Synthesis

  • Qualitative choice logic - Artificial Intelligence, 2004. [All Versions].
  • Inductive Logic - Plato Stanford. A computational philosophy account on Inductive Logic, which is a logic of evidential support.
  • First-order Model Theory - Plato Stanford. A computational philosophy account on First-order Model Theory, which is a branch of mathematics that deals with the relationships between descriptions in first-order languages and the structures that satisfy these descriptions.
  • Paraconsistent Logic - Plato Stanford. A computational philosophy account on Paraconsistent Logic, where any logic is paraconsistent as long as it is not explosive.
  • Logical Consequence - Plato Stanford. A computational philosophy account on Logical Consequence, which is about the relation between premises and conclusions in valid arguments.
  • Logic Pluralism - Plato Stanford. A computational philosophy account on Logic Pluralism, which is the view that there is more than one correct logic.
  • The Emergence of First-Order Logic - Plato Stanford. A computational philosophy account on the emergence of first-order logic, mainly about first-order logic is natural retrospect.
  • Second-order and Higher-order Logic - Plato Stanford.
  • Program Synthesis - Foundations and Trends in Programming Languages, 2017. [All Versions]. Sumit Gulwani's comprehensive review on program synthesis.
  • The Discovery of the Equator or Concept Driven Learning - IJCAI'83, 1983. [All Versions]. The original paper on second-order metarules.
  • Meta-interpretive learning: application to grammatical inference - Machine Learning, 2014. [All Versions]. Stephen Muggleton's original paper on Meta-Interpretive Learning (MIL).
  • Inductive logic programming at 30: a new introduction - Journal of Artificial Intelligence Research, 2020. [All Versions]. A 30-year comprehensive review on Inductive Logic Programming.
  • DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning - 2020. [All Versions]. A incremental learning version of Bayesian program learning.
  • Papers

    Knowledge Representation

  • Self-Knowledge - Plato Stanford. A computational philosophy account on self-knowledge, which standardly refers to knowledge of one's own mental states—that is, of what one is feeling or thinking, or what one believes or desires.
  • Handbook of Knowledge Representation - Elsevier, 2008. [All Versions]. A pragmatical handbook for all kinds of knowledge representation modes.
  • Logic and Ontology - Plato Stanford. A computational philosophy account on logic and ontology, mainly about the intersections of logic and ontology in many significant philosophy problems.
  • The Language of Thought Hypothesis - Plato Stanford. A computational philosophy account on the laugnage of though hypothesis, which proposes that thinking occurs in a mental language.
  • Scientific Representation - Plato Stanford. A computational philosophy account on scientific representation, focusing on how scientific models represent their target systems.
  • Supervenience - Plato Stanford. A computational philosophy account on supervenience, where a set of properties A supervenes upon another set B just in case no two things can differ with respect to A-properties without also differing with respect to their B-properties.
  • Dialogical Logic - Plato Stanford. A computational philosophy account on dialogical logic, which is a dialogue-based approach to logic and argumentation rooted in a research tradition that goes back to dialectics in Greek Antiquity, when problems were approached through dialogues in which opposing parties discussed a thesis through questions and answers.
  • Temporal Logic - Plato Stanford.
  • Situation Calculus - Wikipedia. Wikipedia on Situation Calculus, which is a logic formalism designed for representing and reasoning about dynamical domains.
  • Modal Logic - Plato Stanford. A computational philosophy account on Modal Logic, which is the study of the deductive behavior of the expressions 'it is necessary that' and 'it is possible that'.
  • Epistemic Logic - Plato Stanford. A computational philosophy account on Epistemic Logic, which is a subfield of epistemology concerned with logical approaches to knowledge, belief and related notions.
  • The Perception of Relations - Trends in Cognitive Sciences, 2021. [All Versions]. Chaz Firestone's review on the perception of relation, in constrast to the conventional reasoning view.
  • Qualitative Simulation - Artificial Intelligence, 1986. [All Versions]. Benjamin Kuipers' original paper on qualitative reasoning.
  • Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge - MIT Press, 1994. [All Versions]. Benjamin Kuipers' comprehensive book on qualitative reasoning.
  • Answer Set Programming - ICLPNR'99, 1999. [All Versions]. The original paper on Answer Set Programming (ASP).
  • Action Languages, Answer Sets, and Planning - The Logic Programming Paradigms, 1999. [All Versions].
  • The discovery of structural form - Proceedings of National Academy of Sciences, 2008. [All Versions]. Chales Kemp's review on theory induction.
  • Theory Acquisition and the Language of Thought - CogSci'08, 2008. [All Versions].
  • Biocoder: A programming language for standardizing and automating biology protocols - Journal of Biological Engineering, 2010. [All Versions]. Microsoft's programming language for representing biology protocols.
  • Papers

    Cognitive Development

  • Machine Common Sense Concept Paper - DARPA, 2018. [All Versions]. DARPA's perspective on integrating core knowledge from development psychology into machine intelligence systems.
  • Cognitive Development - Wikipedia.
  • Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory - Psychological Bulletin, 2012. [All Versions]. Alison Gopnik's review on the constructivism idea of developmental research.
  • Towards a rational constructivist theory of cognitive development - Psychological Review, 2019. [All Versions]. Fei Xu's review extending Gopnik's view of constructivism, with the rationality as constraint.
  • The origins of inquiry: inductive inference and exploration in early childhood - Trends in Cognitive Sciences, 2012. [All Versions]. Laura Schulz's review on children's exploratory play.
  • Play, Curiosity, and Cognition - Annual Review of Developmental Psychology, 2020. [All Versions]. Laura Schulz's review on children's exploratory play, which proposes a new perspective on exploratory play to explain the emergence of irrational behaviors in play.
  • Academic Tools

    Literature Management

  • Litmaps - Litmap Ltd. For interactive literature map construction and linked document management.
  • Academic Tools

    Knowledge Management

  • Where Does Niklas Luhmann's Card Index Come From? - Erudition and the Republic of Letters, 2018. [All Versions]. A simplified introduction on Luhmann's Zettelkasten.
  • Niklas Luhmann's Card Index: The Fabrication of Serendipity - Sociologica, 2018. [All Versions].
  • Papers

    Intrinsic Motivation

  • Intrinsic Exploration as Empowerment in a Richly Structured Online Game - 2022. [All Versions].
  • Multi-task reinforcement learning in humans - Nature Human Behavior, 2021. [All Versions].
  • Papers

    Learning in the Open World

  • Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly - IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. [All Versions]. A comprehensive review on zero-shot learning.
  • Generalizing from a few examples: A survey on few-shot learning - ACM Computing Survey, 2020. [All Versions].
  • Towards Open World Recognition - CVPR'15, 2015. [All Versions]. The first paper introducing the problem of open-world recognition.
  • In the Wild: From ML Models to Pragmatic ML Systems - ICLR'20, 2020. [All Versions]. A comprehensive review on incremental machine learning.
  • Papers

    Commonsense Knowledgebase

  • CYC: A Large-Scale Investment in Knowledge Infrastructure - Communications of the ACM, 1995. [All Versions]. The first attempt to build large-scale commonse knoweldgebase from human knowledge.
  • The Public Acquisition of Commonsense Knowledge - Proceedings of AAAI Spring Symposium on Acquiring (and Using) Linguistic (and World) Knowledge for Information Access, 2002. [All Versions]. The first attempt for acquring commonsense knowlege from humans' activities on the internet.
  • Papers

    Intuitive Physics

  • Intuitive Physics: Current Research and Controversies - Trends in Cognitive Sciences, 2018. [All Versions]. Hongjing Lu's review on intuitive physics.
  • Simulation as an engine of physical scene understanding - Proceedings of National Academy of Sciences, 2013. [All Versions]. [Appendix]. The first attempt to computationally simulate intuitive physics.
  • Functional neuroanatomy of intuitive physical inference - Proceedings of National Academy of Sciences, 2016. [All Versions]. A piece of evidence for the functional part of intuitive physics in human brain.
  • Mind Games: Game Engines as an Architecture for Intuitive Physics - Trends in Cognitive Sciences, 2017. [All Versions]. Tomer Ullman's review on simulation-based intuitive physics.
  • Limits on Simulation Approaches in Intuitive Physics - Cognitive Psychology, 2021. [All Versions]. Ernest Davis's perspective against intuitive physics, that physcial reasoning is logical reasoning instead of intuition.
  • Papers

    AI Commonsense Reasoning

  • Representations of Commonsense Knowledge - Morgan Kaufmann, 1990. [All Versions]. A classic book on commonsense knowledge.
  • Towards a theory of commonsense visual reasoning - FSTTCS, 1990. [All Versions]. The original paper on visual commonsense.
  • Commonsense reasoning and commonsense knowledge in artificial intelligence - Communications of the ACM, 2015. [All Versions]. Gary Marcus's review on commonsense knowledge in AI.
  • Abductive Commonsense Reasoning - ICLR'20, 2020. [All Versions]. Abductive commonsense reasoning on large language models.
  • Experience Grounds Language - EMNLP'20, 2020. [All Versions]. A perspective on the furture of computational linguistics research---commonsense-driven and embodied language.
  • Papers

    Analogy

  • Metaphor - Plato Stanford. A computational philosophy account on Metaphor, a poetically or rhetorically ambitious use of words, a figurative as opposed to literal use.
  • Analogy and Analogical Reasoning - Plato Stanford. A computational philosophy account on Analogy, a comparison between two objects, or systems of objects, that highlights respects in which they are thought to be similar.
  • A Cognitive Theory of Metaphor - MIT Press, 1985. [All Versions]. A cognitive account on Metaphor.
  • The structure-mapping engine: Algorithm and examples - Artificial Intelligence, 1989. [All Versions]. A computational implementation of analogy.
  • Structure mapping in analogy and similarity - American Psychologist, 1997. [All Versions]. A perspective unifying analogy and similarity judgement.
  • A theory of relation learning and cross-domain generalization - Psychological Review, 2022. [All Versions]. A comprehensive review on the perspective of treating analogy as cross-domain generalization.
  • Emergence of analogy from relation learning - Proceedings of National Academy of Sciences, 2019. [All Versions]. Analogy feature in language models.
  • Analogies Explained: Towards Understanding Word Embeddings - ICML'19, 2019. [All Versions]. Explaining the analogy capability in word embeddings.
  • Analogy-preserving Semantic Embedding for Visual Object Categorization - ICML'13, 2013. [All Versions]. The first application of analogy to machine learning.
  • Analogy between concepts - Artificial Intelligence, 2019. [All Versions]. A mathematical account on analogy.
  • Preschoolers and adults make inferences from novel metaphors - CogSci'22, 2022. [All Versions]. A piece of evidence that understanding metaphors is capable for different cognitive development phases.
  • Papers

    Theory of Mind

  • Theory of Mind - Wikipedia. Wikipedia on Theory of Mind (ToM), a cognitive capability that estimating others' goal, belief, and desire.
  • Papers

    Gestalt

  • Gestalt theory - A source book of Gestalt psychology, 1938. [All Versions]. The original book on Gestalt psychology.
  • Gestalt Psychology - Psychologische Forschung, 1967. [All Versions]. Wolfgang Köhler's review on Gestalt psychology.
  • Deep Learning: How the Mind Overrides Experience - Cambridge University Press, 2011. [All Versions].
  • Papers

    Rationality

  • Bounded Rationality - Plato Stanford. A computational philosophy account on Bounded Rationality, an elementary hypothesis of human intelligence in psychology and ecology.
  • Instrumental Rationality - Plato Stanford. A computational philosophy account on Instrumental Rationality, a dabate on whether an agent's decision is made intentionally or out of rational coherence.
  • Computational rationality: A converging paradigm for intelligence in brains, minds, and machines - Science, 2015. [All Versions]. A comprehensive review on the rationality of Bayesian computational models.
  • Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources - Behavioral and Brain Sciences, 2020. [All Versions]. A resource-rational account on interpreting human intelligence.
  • Papers

    Scaling Up Behavioral Studies

  • Scaling up psychology via Scientific Regret Minimization - Proceedings of National Academy of Sciences, 2020. [All Versions]. The statistical and ecological basis for scaling up behavioral studies.
  • Using large-scale experiments and machine learning to discover theories of human decision-making - Science, 2021. [All Versions]. A piece of evidence for the merits brought by large-scale behavioral studies in social science.
  • Visual Search at Pinterest - KDD'15, 2015. [All Versions]. Large scale user study in the development of the recommendations system by Pinterest.
  • Papers

    Question Answering

  • Searching large hypothesis spaces by asking questions - CogSci'16, 2016. [All Versions]. A behavioral study for the 20 questions game.
  • Asking and evaluating natural language questions - CogSci'16, 2016. [All Versions]. A behavioral study for the battleship game.
  • Papers

    Human-Machine Comparison

  • Elimination by aspects: A theory of choice - Psychological Review, 1972. [All Versions]. Herbert Simon's early experiments on computer aided behavioral studies.
  • Papers

    Virtual Reality

  • Virtual reality in behavioral neuroscience and beyond - Nature Neuroscience, 2002. [All Versions]. A classic review on the early applications of Virtual Reality to behavioral studies.
  • The psychology of virtual reality - The psychology of technology: Social science research in the age of Big Data (pp. 155–193), American Psychological Association, 2022. [All Versions]. Jeremy Bailenson's review on the applications of Virtual Reality to behavioral studies.
  • Papers

    Meta Learning

  • Automated Reinforcement Learning (AutoRL): A Survey and Open Problems - 2022. [All Versions]. A comprehensive review on AutoRL.
  • Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks - ICML'17, 2017. [All Versions]. Chelsea Finn's original paper on Model-Agnostic Meta-Learning (MAML).
  • Bayesian Model-Agnostic Meta-Learning - NIPS'18, 2018. [All Versions]. A Bayesian account on MAML.
  • Papers

    Evolutionary Intelligence

  • Evolutionary trade-offs, Pareto optimality, and the geometry of phenotype space - Science, 2012. [All Versions]. A classic paper correlating biological trade-offs with the evolution of pareto optimality.
  • Pareto optimality in multiobjective problems - Applied Mathematics and Optimization, 1977. [All Versions]. The original paper on the pareto optimality in multiobjective problems.
  • Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies - IEEE Transactions on Systems, Man, and Cybernetics, 2008. [All Versions]. A comprehensive review on the application of pareto optimality to multiobjective machine learning.
  • Papers

    Trustworthy AI

  • A tale of two explanations: Enhancing human trust by explaining robot behavior - Science Robotics, 2019. [All Versions]. The original paper on human believable robot, a result of the DAPAR-XAI.
  • X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust - 2019. [All Versions]. Introducing the idea of AI estimating human users' knowledge in to explainable AI.
  • Papers

    Strong Machine Learning

  • Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP - Machine Learning, 2018. [All Versions]. Stephen Muggleton's account of ultra-strong machine learning, which not only learns human understandable knowledge, but also improves human performance on the corresponding tasks.
  • NBDT: Neural-Backed Decision Trees - NIPS'20, 2020. [All Versions]. [codestars544]. Expliciting the decision process of a decision tree through neural networks.
  • Papers

    Explainable Deep Learning

  • Network dissection: Quantifying interpretability of deep visual representations - CVPR'17, 2017. [All Versions]. [Project]. [Dataset: Places365]. The original paper on visualizing the class activation maps to explain convolutional neural networks.
  • Understanding the role of Individual Units in a Deep Neural Network - Proceedings of National Academy of Sciences, 2020. [All Versions]. David Bau's review on network dissection for discriminative and generative models.
  • Zoom In: An Introduction to Circuits - Distill, 2020. [All Versions]. A perspective on treating neural networks as circuits.
  • Compositional Explanations of Neurons - NIPS'20, 2020. [All Versions]. [Projectstars22]. A concept-composition version of network dissection.
  • Noise or Signal: The Role of Backgrounds in Image Classification - ICLR'21, 2021. [All Versions]. [Project]. A perspective on image background provides strong clue for foreground classification.
  • Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation - NIPS'18, 2018. [All Versions]. Maching the learned pattern of neurons in different neural networks.
  • Papers

    Inverse Reinforcement Learning

  • Bayesian Inverse Reinforcement Learning - IJCAI'07, 2007. [All Versions]. A Bayesian account on classic inverse reinforcement learning.
  • Papers

    Dual-Coding Theory

  • Mental Representations: A Dual Coding Approach - Oxford University Press, 1990. [All Versions]. The original book on dual coding theory, in the neuroscience account of mental representation.
  • Dual coding of knowledge in the human brain - Trends in Cognitive Sciences, 2021. [All Versions]. Yanchao Bi's review on neuroscience experiments on dual coding theory.
  • Two Forms of Knowledge Representations in the Human Brain - Neuron, 2020. [All Versions]. Illustrating language-derived and sensory-derived knowledge.
  • Papers

    Neural-Symbolic AI

  • Regression Analysis for Interval-Valued Data - Data Analysis, Classification, and Related Methods, 2000. [All Versions]. The original paper on symbolic regression.
  • Symbolic data analysis: what is it? - Proceedings in Computational Statistics, 2006. [All Versions].
  • DeepProbLog: Neural Probabilistic Logic Programming - NIPS'18, 2018. [All Versions]. The original paper on neuro-symbolic probabilistic programming.
  • Learning Explanatory Rules from Noisy Data - Journal of Artificial Intelligence Research, 2018. [All Versions]. The original paper for differential Inductive Logic Programming.
  • Bridging Machine Learning and Logical Reasoning by Abductive Learning. - NIPS'19, 2019. [All Versions]. [Slides]. [Codestars70]. The original paper on Abductive Learning, a derivative-free approach for neuro-symbolic learning.
  • Learning by Abstraction: The Neural State Machine - NIPS'19, 2019. [All Versions].
  • AI Feynman: A physics-inspired method for symbolic regression - Science Advances, 2019. [All Versions]. A general approach for neuro-symbolic formula synthesis.
  • Neural Production Systems - ICML'21, 2021. [All Versions]. Yoshua Bengio's perspective on slot attention model as a general production system.
  • Jul 22nd

    Papers

    Human-Level Problem Solving

  • Insightful problem solving and creative tool modification by captive nontool-using rooks - Proceedings of National Academy of Sciences, 2009. [All Versions]. [Supplementary Material]. A piece of evidence on creative tool use in intelligent animals.
  • Elements of a theory of human problem solving - Psychological Review, 1958. [All Versions]. Herbert Simon's original idea on human problem solving.
  • Human Problem Solving - Englewood Cliffs, NJ: Prentice-hall, 1972. [All Versions]. Herbert Simon's classic idea of human problem solving as search.
  • Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty - Science, 1974. [All Versions]. Daniel Kahneman's classic idea of prospective theory.
  • Computational evidence for hierarchically structured reinforcement learning in humans - Proceedings of National Academy of Sciences, 2020. [All Versions]. A piece of evidence on hierarchical human planning.
  • People construct simplified mental representations to plan - Nature, 2022. [All Versions]. A computational account on rational problem representation in human planning.
  • Rapid trail-and-error learning with simulation supports flexible tool use and physical reasoning. - Proceedings of National Academy of Sciences, 2020. [All Versions]. [Project]. [Appendix]. A computational account on rapid trail-and-error problem solving with a noisy prior model.
  • Papers

    Reinforcement Learning

  • Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability - NIPS'21, 2021. [All Versions]. A formal treatment on the generalization problem in reinforcement learning.
  • Reinforcement learning: An introduction - MIT Press, 2018. [All Versions]. Richard Sutton's comprehensive book on reinforcement learning.
  • Reinforcement learning: A survey - Journal of Artificial Intelligence Research, 1996. [All Versions]. Leslie Kaelbling's review on reinforcement learning.
  • Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning - Artificial Intelligence, 1999. [All Versions]. The original paper on operation reinforcement learning.
  • Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review - 2018. [All Versions]. [Slides]. Sergey Levine's tutorial on treating reinforcement learning probabilisticly.
  • On the Expressivity of Markov Reward - NIPS'21, 2021. [All Versions]. A formal treatment of tasks and rewards in reinforcement learning modeling.
  • Constrained Policy Optimization - ICML'17, 2017. [All Versions]. The original paper on constrained reinforcement learning (safe reinforcement learning).
  • The Quest for a Common Model of the Intelligent Decision Maker - Multi-disciplinary Conference on Reinforcement Learning and Decision Making'22, 2022. [All Versions]. Richard Sutton's perspective on the future directions of reinforcement learning research.
  • On Monte Carlo Tree Search and Reinforcement Learning - Journal of Artificial Intelligence Research, 2017. [All Versions].
  • Papers

    Inverse Reinforcement Learning

  • Apprenticeship Learning via Inverse Reinforcement Learning - ICML'04, 2004. [All Versions]. Pieter Abbeel and Andrew Ng's original paper on inverse reinforcement learning (IRL).
  • Papers

    Intrinsic Motivation

  • Adapting Behavior via Intrinsic Reward: A Survey and Empirical Study - Journal of Artificial Intelligence Research, 2020. [All Versions].
  • Curiosity-driven Exploration by Self-supervised Prediction - ICML'17, 2017. [All Versions]. The original paper on curiosity as intrinsic motivation.
  • UCB Exploration via Q-Ensembles - 2017. [All Versions].
  • Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning - NIPS'15, 2015. [All Versions]. The original paper on empowerment as intrinsic motivation.
  • Intrinsically Motivated Reinforcement Learning - NIPS'04, 2004. [All Versions]. A comprehensive review on intrinsic reward functions in classic reinforcement learning.
  • Papers

    Planning

  • From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning - Journal of Artificial Intelligence Research, 2018. [All Versions]. Leslie Kaelbling's review on hierarchical Task-and-Motion-Planning (hierarchical TAMP).
  • Integrated Task and Motion Planning - Annual Review of Control, Robotics, and Autonomous Systems, 2021. [All Versions]. Leslie Kaelbling's review on Task-and-Motion-Planning (TAMP).
  • Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning - Robotics: Science and Systems, 2018. [All Versions].
  • Papers

    Pragmatics

  • Pragmatics - Plato Stanford. A computational philosophy account of Pragmatics, whilch studies utterances in specific contexts.
  • Predicting Pragmatic Reasoning in Language Games - Science, 2012. [All Versions]. The original paper on Rational Speech Act (RSA).
  • Pragmatic Language Interpretation as Probabilistic Inference - Trends in Cognitive Sciences, 2016. [All Versions]. Noah Goodman and Micheal Frank's review on Rational Speech Act.
  • Processing gradable adjectives in context: A visual world study - Semantics and Linguistic Theory, 2016. [All Versions]. Adjective understanding as a rational inference in the context.
  • Social Pragmatics: Preschoolers Rely on Commonsense Psychology to Resolve Referential Underspecification - Child Development, 2019. [All Versions]. A piece of evidence for children's capability on social pragmatics.
  • Pragmatic Issue-Sensitive Image Captioning - ACL Findings: EMNLP'20, 2020. [All Versions]. Application of Rational Speech Act to Image Captioning.
  • Disentangling contributions of visual information and interaction history in the formation of graphical conventions - CogSci'19, 2019. [All Versions].
  • Papers

    Language Compositionality

  • Compositionality - Plato Stanford. A computational philosophy account on compositionality, one of the distinctive feature of language.
  • The Principle of Semantic Compositionality - Topoi, 1994. [All Versions]. The original paper on the principle of semantic compositionality.
  • On The Emergence Of Compositionality - Proceedings of the Evolution of Language Conference'06, 2006. [All Versions]. The original paper on the emergence of compositionality.
  • Multi-Agent Cooperation and the Emergence of (Natural) Language - ICLR'17, 2017. [All Versions]. The original paper on the emergence of language in multi-agent reinforcement learning.
  • Papers

    Pointing & Pantomime

  • Twelve-month-olds communicate helpfully and appropriately for knowledgeable and ignorant partners - Cognition, 2008. [All Versions]. The original paper on child pointing.
  • Papers

    Learning with Cognitive Plausibility

  • BONGARD-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning - NIPS'20, 2020. [All Versions].
  • Accuracy and Precision - Wikipedia. Wikipedia on the distinctions and the trade-off between accuracy and precision.
  • Recognition-by-Components: A Theory of Human Image Understanding - Psychological Review, 1987. [All Versions]. The original paper on the recognition-by-components theory.
  • Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense - Engineering, 2020. [All Versions]. Yixin Zhu and Song-Chun Zhu's review on visual commonsense.
  • Self-supervised Learning Through the eyes of a Child - NIPS'20, 2020. [All Versions]. Concept learning through near-natural co-occurrence frequency estimation.
  • Learning and development in networks: The importance of starting small. - Cognition, 1993. [All Versions]. The original paper on the idea of curriculum learning.
  • Curriculum Learning - ICML'09, 2009. [All Versions]. The original paper applying the idea of curriculum learning to machine learning.
  • Inferring "Dark Matter" and "Dark Energy" from Videos - ICCV'13, 2013. [All Versions]. The original paper on latent state discovery from videos.
  • Papers

    Visual Communication

  • The Interactive Evolution of Human Communication Systems - Cognitive Science, 2010. [All Versions]. Nicolas Fay's original paper on iconicity.
  • Iconicity: From sign to system in human communication and language - Pragmatics & Cognition, 2014. [All Versions]. Nicolas Fay's account on the emergence of iconic language.
  • Graphical Language Games: Interactional Constraints on Representational Form - Cognitive Science, 2007. [All Versions]. The first paper introducing the graphical language game.
  • Pixelor: A Competitive Sketching AI Agent. So you think you can beat me? - ACM SIG Graph, 2020. [All Versions]. [Project]. Rationality in feature sketching.
  • Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - Computational Brain & Behavior, 2020. [All Versions]. A computational account on the rational behavior in graphical language games.
  • Emergent Graphical Conventions in a Visual Communication Game - 2021. [All Versions]. A computational account on the emergence of iconic language.
  • Communicating artificial neural networks develop efficient color-naming systems - Proceedings of National Academy of Sciences, 2021. [All Versions]. Simulating the emergence of code as the communication bottleneck in color learning task.
  • Bridging cultural and cognitive perspectives on similarity reasoning - CogSci'22, 2022. [All Versions].
  • Papers

    Dimensionality Reduction

  • Deep Learning and the Information Bottleneck Principle - IEEE Information Theory Workshop'15, 2015. [All Versions]. The first paper identifying the problem of information bottleneck in representation learning.
  • On the information bottleneck theory of deep learning - Journal of Statistical Mechanics: Theory and Experiment, 2019. [All Versions].
  • A global geometric framework for nonlinear dimensionality reduction - Science, 2000. [All Versions]. The original paper on spectrum clustering.
  • Reducing the dimensionality of data with neural networks - Science, 2006. [All Versions]. The original paper on Variational Autoencoder.
  • Representation Learning: A Review and New Perspectives - IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013. [All Versions]. Yoshua Bengio's review on representation learning.
  • Representation Learning: A Statistical Perspective - Annual Review of Statistics and Its Application, 2020. [All Versions]. Song-Chun Zhu and Ying Nian Wu's review on representation learning, in an account of statistics.
  • Papers

    Visual Complexity

  • Visual complexity: a review - Psychological Bulletin, 2006. [All Versions]. [APA]. A psychological account on visual complexity.
  • Seeing and speaking: How verbal “description length” encodes visual complexity - Journal of Experimental Psychology, 2022. [All Versions]. [APA]. Empirical evidencs showing the relation between visual complexity and description length.
  • How variability shapes learning and generalization - Trends in Cognitive Sciences, 2022. [All Versions]. A comprehensive review on the trade-off between variability and generalization ability.
  • Papers

    Theory

  • An introduction to Kolmogorov complexity and its applications - Springer, 2008. [All Versions]. The introductory book for Algorithmic Information Theory, especially the Kolmogorov complexity theory.
  • Complexity and the representation of patterned sequences of symbols - Psychological Review, 1972. [All Versions]. Herbert Simon's review on subjective complexity.
  • Algorithmic Information Theory - IBM Journal of Research and Development, 1977. [All Versions]. Chaitin's original paper on Algorithmic Information Theory.
  • Papers

    Nonparametric Model

  • Hierarchical topic models and the nested Chinese restaurant process - NIPS'03, 2003. [All Versions]. The original paper for nested Chinese restaurant process.
  • Infinite Latent Feature Models and the Indian Buffet Process - Gatsby Computational Neuroscience Unit Technical Report 2005-001, 2005. [All Versions].
  • The Indian Buffet Process: An Introduction and Review - Journal of Machine Learning Research, 2011. [All Versions]. Tom Griffiths and Zoubin Ghahramani's review on infinite models, including the Chinese Restaurant Process (CRP) and the Indian Buffet Process (IBP).
  • Nonparametric Bayesian Logic - UAI'05, 2005. [All Versions]. The first paper integrating logic into non-parametric model.
  • Statistical Predicate Invention - ICML'07, 2007. [All Versions]. Treating predicate invention as a non-parametric problem, in the account of statistics.
  • A Bayesian Analysis of Some Non-parametric Problems - The Annals of Statistics, 1973. [All Versions]. A classic review on non-parametric problems.
  • Mixtures of Dirichlet Process with Applications to Bayesian Nonparametric Problems - The Annals of Statistics, 1974. [All Versions]. The original paper on Dirichlet Process modeling for non-parametric problems.
  • Latent Semantic Indexing: A Probabilistic Analysis - Journal of Computer and System Sciences, 2000. [All Versions]. The original paper on hierarchical topic model.
  • Finding scientific topics - Proceedings of the National Academy of Sciences, 2004. [All Versions]. Application on scientific paper ananlysis for hierarchical topic model.
  • Papers

    Bayesian Optimization

  • A Tutorial on Bayesian Optimization - 2018. [All Versions].
  • Practical Bayesian Optimization of Machine Learning Algorithms - NIPS'12, 2012. [All Versions]. The original paper for applying Bayesian optimization to machine learning hyperparameter selection.
  • Papers

    Generative Model

  • From information scaling of natural images to regimes of statistical models - Quarterly of Applied Mathematics, 2008. [All Versions]. A statistical account for the shift from textons to texture.
  • A Theory of Generative ConvNet - ICML'16, 2016. [All Versions].
  • Learning Latent Space Energy-Based Prior Model - NIPS'20, 2020. [All Versions]. [Project]. [Codestars26]. A milestone paper on Latent Energy-Based Model.
  • Image segmentation by data-driven markov chain monte carlo - IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. [All Versions]. Classic method for image segmentation via generative modeling.
  • A Tutorial on Energy-Based Learning - Predicting Structured Data, MIT Press, 2006. [All Versiosn]. Yann LeCun's tutorial on energy-based learning.
  • Analysis of Langevin Monte Carlo via Convex Optimization - Journal of Machine Learning Research, 2019. [All Versions].
  • Where do hypotheses come from? - Cognitive Psychology, 2017. [All Versions]. A Bayesian account for modeling basic rules as the hypothesis space.
  • Filters, random fields and maximum entropy (FRAME): Towards a unified theory for texture modeling - International Journal of Computer Vision, 1998. [All Versions]. Song-Chun Zhu's original paper on energy-based generative texture modeling.
  • Object Perception as Bayesian Inference - Annual Review of Psychology, 2004. [All Versions]. Alan Yuille's review on Bayesian object perception.
  • A tale of three probabilistic families: Discriminative, descriptive, and generative models - Quarterly of Applied Mathematics, 2018. [All Versions]. Ying Nian Wu's review on three families of statistical modeling.
  • Papers

    Bayesian Induction

  • Bayesian Epistemology - Plato Stanford. A computational philosophy account on the nature of uncertainty modeling in Bayesian Epistemology.
  • Probabilistic machine learning and artificial intelligence - Nature, 2015. [All Versions]. Zoubin Ghahramani's review on Bayesian machine learning.
  • Generalization, similarity, and Bayesian inference - Behavioral and Brain Sciences, 2001. [All Versions]. Josh Tenenbaum's review on Bayesian generalization.
  • Bayesian modeling of human concept learning - NIPS'98, 1998. [All Versions]. Original paper on Bayesian generalization.
  • Rules and Similarity in Concept Learning - NIPS'99, 1999. [All Versions]. Unifying rule-based and similarity-based generalization via Bayesian generalization.
  • Theory-based Bayesian models of inductive learning and reasoning - Trends in Cognitive Sciences, 2006. [All Versions]. Josh Tenenbaum's review on Bayesian theory induction.
  • Word learning as Bayesian inference - Psychological Review, 2007. [All Versions]. [APA]. Fei Xu's review on Bayesian word learning.
  • How to Grow a Mind: Statistics, Structure, and Abstraction - Science, 2011. [All Versions]. Josh Tenenbaum's review on Bayesian theory induction.
  • Human-level concept learning through probabilistic program induction. - Science, 2015. [All Versions]. [Supplementary Material]. Bayesian program induction for few-shot learning.
  • Building Machines That Learn and Think Like People - Behavioral and Brain Sciences, 2017. [All Versions]. Brenden Lake and Josh Tenenbaum's review on Bayesian modeling.
  • Papers

    Applications in AI

  • Interpretation as abduction - Artificial Intelligence, 1993. [All Versions]. A computational account on interpretation as Abduction.
  • Probabilistic Horn abduction and Bayesian networks - Artificial Intelligence, 1993. [All Versions]. Casual abduction in Bayesian networks.
  • Abductive Logic Programming - Journal of Logic Computation, 1992. [All Versions]. The original paper in ALP.
  • ACLP: Abductive Constraint Logic Programming - The Journal of Logic Programming, 1999. [All Versions]. The original paper in ACLP.
  • Abduction in Logic Programming - Computational Logic, 2002. [All Versions]. The revised version of the ALP paper.
  • Machine Translation Using Abductive Inference - COLING, 1990. [All Versions]. An application of abduction in language translating.
  • Anomaly detection through explanations - Ph.D Dissertation MIT, 2018. [All Versions]. An application of abduction in anomaly detection.
  • Functional genomic hypothesis generation and experimentation by a robot scientist - Nature, 2004. [All Versions]. A canonical application of logical abduction on biodesign.
  • Highly accurate protein structure prediction with AlphaFold - Nature, 2021. [All Versions]. A canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.
  • Papers

    Scientific Discovery

  • Abduction and styles of scientific thinking - Synthese, 2021. [All Versions]. A computational philosophy account connecting Abduction and scientific thinking.
  • Scientific Discovery - Plato Stanford. A computational philosophy account on Scientific Discovery, the process or product of successful scientific inquiry, sometimes an Abduction-like (Explanation) thinking pattern.
  • Scientific discovery: Computational explorations of the creative processes - MIT Press, 1987. [All Versions]. A computational account unifying Scientific Discovery with the creativity feature of Abduction.
  • Induction: Processes of Inference, Learning, and Discovery - MIT Press, 1989. [All Versions]. An Induction account of Scientific Discovery.
  • Complexity Management in a Discovery Task - CogSci'92, 1992. [All Versions]. Advanced experiments on dual space search.
  • A dual-space model of iteratively deepening exploratory learning - International Journal of Human-Computer Studies, 1996. [All Versions]. Iterative version (in depth and in width) of dual space search.
  • Heuristics for Scientific Experimentation: A Developmental Study - Cognitive Psychology, 1993. [All Versions]. A piece of evidence on children have basic scientific thinking skills.
  • A 4-Space Model of Scientific Discovery - CogSci'95, 1995. [All Versions]. Extending the dual space search.
  • When to trust the data: Further investigations of system error in a scientific reasoning task - Memory & Cognition, 1996. [All Versions]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  • Confirmation, disconfirmation, and information in hypothesis testing - Psychological Review, 1987. [All Versions]. A psychological account on hypothesis testing.
  • Children and adults as intuitive scientists - Psychological Review, 1989. [All Versions]. A perspective against search as scientific thinking.
  • Models of Discovery: And Other Topics in the Methods of Science - Springer, 1977. [All Versions]. The original book on search as scientific thinking.
  • Dual Space Search During Scientific Reasoning - Cognitive Science, 1988. [All Versions]. The original paper on the dual space search as scientific thinking theory.
  • Papers

    Rationalization

  • Imagination and the generation of new ideas - Cognitive Development, 2015. [All Versions]. A piece of evidence for rationalization in childhood.
  • Coalescing the Vapors of Human Experience into a Viable and Meaningful Comprehension - CogSci'16, 2016. [All Versions]. Constrainted thinking as rationalization.
  • How We Know What Not To Think - Trends in Cognitive Sciences, 2019. [All Versions]. A comprehensive review on rationalization.
  • Rationalization is rational - Behavioral and Brain Sciences, 2020. [All Versions]. A rationality account on rationalization.
  • Why Imaginary Worlds? The psychological foundations and cultural evolution of fictions with imaginary worlds - Behavioral and Brain Sciences, 2021. [All Versions]. A review of rationalization as imaginary worlds in fictions.
  • Papers

    Explanation

  • Explanation-seeking curiosity in childhood - Current Opinion in Behavioral Sciences, 2020. [All Versions]. A piece of developmental pshchological evidence for Abduction in young children.
  • Probabilistic alternatives to Bayesianism: the case of explanationism - Frontiers in Psychology, 2015. [All Versions]. A non-Bayesian account of Abduction.
  • A Probabilistic Theory of Abductive Reasoning - ICAART, 2021. [All Versions]. A probabilistic perspective for interpreting Abductive Reasoning.
  • Patterns of abduction - Synthese, 2007. [All Versions]. A categorization for Abduction in the account of pure philosophy.
  • Abduction, Induction, and Analogy - Model-Based Reasoning in Science and Technology, 2010. [All Versions]. The distinctions and relations between Abduction, Induction, and Analogy.
  • Remembrance of inferences past: Amortization in human hypothesis generation - Cognition, 2018. [All Versions]. A rational account of human hypothesis generation.
  • Explanation, updating, and accuracy - Journal of Cognitive Psychology, 2016. [All Versions].
  • Best, second-best, and good-enough explanations: How they matter to reasoning - Journal of Experimental Psychology, 2018. [All Versions]. A subjective probability account of Abduction.
  • How explanation guides belief change - Trends in Cognitive Sciences, 2021. [All Versions]. A review on the subjective probability account of Abduction.
  • On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - Synthese, 2011. [All Versions].
  • Explanation and Abductive Inference - The Oxford Handbook of Thinking and Reasoning, 2012. [All Versions]. A handbook on the formulations of Abduction.
  • Probabilistic models of cognition: Conceptual foundations - Trends in Cognitive Sciences, 2006. [All Versions]. A Bayesian account of Abduction.
  • The structure and function of explanations - Trends in Cognitive Sciences, 2006. [All Versions]. Basic computation modes of Abduction.
  • Abductive Reasoning and Learning - Springer, 2000. [All Versions]. An introductory account on abductive reasoning.
  • Abductive Reasoning: Logical Investigations into Discovery and Explanation - Springer, 2006. [All Versions]. An introductory account on abductive reasoning.
  • Explanatory Preferences Shape Learning and Inference - Trends in Cognitive Sciences, 2016. [All Versions]. An account showing that inductive bias is critical for explanation.
  • Scientific Reduction - Plato Stanford. A computational philosophy account on Scientific Reduction, which comes with no explicit boundary with Explanation.
  • Non-monotonic Logic - Plato Stanford. A computational philosophy account on Non-monotonic Logic, a family of formal frameworks devised to capture and represent defeasible inference.
  • Philosophical Writings of Peirce - Courier Corporation, 1955. [All Versions]. Original writings by C. S. Peirce, the establisher of Abduction.
  • The Inference to the Best Explanation - Philosophical Review, 1965. [All Versions]. Lipton's original paper on Inference to the Best Explanation as a special case of Abduction.
  • Inference to the Best Explanation - Routledge, 1991. [All Versions]. Lipton's book on Inference to the Best Explanation as a special case of Abduction.
  • A Study of Thinking - Routledge, 1956. [All Versions]. A classic book on thinking patterns.
  • Abduction - Plato Stanford. A computational philosophy account on Abduction, one of the three thinking patterns besides Induction and Deduction, being unique for its potential to introduce new ideas into current knowledge.
  • Scientific Explanation - Plato Stanford. A computational philosophy account on Scientific Explanation, a canonical application of Abduction.
  • Academic Tools

    Paper Reading

  • How to Read a Paper - ACM SIGCOMM Computer Communication Review, 2007. [All Versions]. A comprehensive tutorial on reading scientific papers.
  • Academic Tools

    Literature Management

  • StateOfTheArt.AI - StateOfTheArtAI. For tracking, collecting and visualizing the development of AI research.
  • Construction of the Literature Graph in Semantic Scholar - NAACL'18, 2018. [All Versions]. Semantic Scholar with extracting feature and metadata from raw paper data.
  • S2ORC: The Semantic Scholar Open Research Corpus - ACL'20, 2020. [All Versions]. An open corpus of academic papers released by Semantic Scholar.
  • Academic Tools

    Knowledge Management

  • Zettelkasten - Wikipedia. Wikipedia on the Zettelkasten method.
  • Roam Research - Roam Research. For linked document management, visualization, and sharing.
  • Foam - Foambubble. For linked document management, visualization, and sharing, opensourced softward built on VSCode.
  • Building a Second Brain - Forte Labs, LLC. Connecting ideas in graphs.
  • Knowledge organization - Wikipedia. Wikipedia on knowledge organization methods.
  • Jul 21st

    Academic Tools

    Paper Reading

  • It's not just you: science papers are getting harder to read - Nature, 2017. [All Versions]. Nature perspective on reading scientific papers.
  • Jul 20th

    Academic Tools

    Knowledge Management

  • Communicating with Slip Boxes - 2019. [All Versions].
  • Jul 18th

    Academic Tools

    Knowledge Management

  • The Zettelkasten Method - Bielefeld University. Relating ideas in graphs and multi-labels.
  • Niklas Luhmann's Card Index: Thinking Tool, Communication Partner, Publication Machine - Forgetting Machines: Knowledge Management Evolution in Early Modern Europe, Brill, 2016. [All Versions].
  • Jul 16th

    Institute & Researcher

    Stanford

  • Jeremy Bailenson - Department of Communication, Stanford, Virtual Human Interaction Lab (VHIL) - Stanford.
  • Papers

    Virtual Reality

  • Virtual reality: A survival guide for the social scientist - Journal of Media Psychology, 2009. [All Versions].
  • Jul 15th

    Papers

    Rationalization

  • Rationalizing constraints on the capacity for cognitive control - Trends in Cognitive Sciences, 2021. [All Versions].
  • Jul 14th

    Papers

    Pointing & Pantomime

  • 12- and 18-Month-Olds Point to Provide Information for Others - Journal of Cognition and Development, 2009. [All Versions].
  • Jul 11th

    Papers

    Commonsense Knowledgebase

  • Open Mind Common Sense: Knowledge Acquisition from the General Public - OTM Confederated International Conferences'02, 2002. [All Versions].
  • Jul 8th

    Academic Tools

    Courses

  • Introduction to Program Synthesis - MIT. Armando Solar-Lezama's elementary course on program synthesis.
  • Institute & Researcher

    UCLA

  • Song-Chun Zhu - Department of Statistics, Department of Computer Science, UCLA.
  • Tao Gao - UCLA, Visual Intelligence Lab - Department of Statistics, Department of Psychology, UCLA.
  • Hongjing Lu - UCLA, Computational Vision and Learning Lab (CVL) - Department of Psychology, Department of Statistics, UCLA.
  • Jul 7th

    Jul 6th

    Papers

    Marr Levels of Analysis

  • From understanding computation to understanding neural circuitry - Neuroscience Research Program Bulletin, 1979. [All Versions].
  • Jul 3rd

    Papers

    Inductive Logic & Program Synthesis

  • AutumnSynth: Synthesis of Reactive Programs with Structured Latent State - NIPS'21 AIPLANS Workshop, 2021. [All Versions].
  • Jul 2nd

    Academic Tools

    Literature Management

  • Goodbye, Microsoft Academic – Hello, open research infrastructure? - LSE Impact Blog, 2021. An interpretation of Microsoft's strategy on research infrastructure.
  • Jun 24th

    Papers

    Inductive Logic & Program Synthesis

  • Program Synthesis with Large Language Models - 2021. [All Versions].
  • Jun 11th

    May 28th

    May 27th

    Papers

    Pragmatics

  • When Lingens meets Frege: communication without common ground - Philosophical Studies, 2021. [All Versions].
  • May 17th

    Papers

    Quantitative Analysis

  • Two lines: A valid alternative to the invalid testing of U-shaped relationships with quadratic regressions - Advances in Methods and Practices in Psychological Science, 2018. [All Versions]. An alternative method to test the statistical significance of U-shaped relationships.
  • May 14th

    Papers

    Theory

  • Visual Pattern Discrimination - IRE Transactions on Information Theory, 1962. [All Versions].
  • May 12th

    People & Book

    John Hopcroft

  • Foundations of Data Science - Cambridge University Press. [All Versions].
  • May 10th

    Papers

    Explainable Deep Learning

  • pytorch-grad-camstars5.6k - 2021. Class Activation Map methods implemented in Pytorch, with many elegant features.
  • May 9th

    Academic Tools

    Literature Management

  • Microsoft Academic Graph - Microsoft Research. Heterogeneous graph containing scientific publication records, citation relationships between those publications, as well as authors, institutions, journals, conferences, and fields of study.
  • An Overview of Microsoft Academic Service (MAS) and Applications - WWW'15, 2015. [All Versios]. Original paper on Microsoft Academic Graph.
  • Semantic Scholar - Allen Institute for AI Research. AI-powered scientific literature research tool.
  • VOSviewer - Leiden University. For constructing and visualizing bibliometric networks.
  • May 8th

    Papers

    Dual-Coding Theory

  • Rational arbitration between statistics and rules in human sequence processing - Nature Human Behavior, 2022. [All Versions].
  • Apr 30th

    Papers

    Theory

  • A Mathematical Theory of Communication - The Bell System Technical Journal, 1948. [All Versions]. Shannon's original paper on Information Theory.
  • Apr 29th

    Apr 26th

    Apr 21st

    Apr 20th

    Papers

    Trustworthy AI

  • CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines - AAAI'20, 2020. [All Versions].
  • Papers

    Strong Machine Learning

  • Beneficial and harmful explanatory machine learning - Machine Learning, 2021. [All Versions].
  • Apr 16th

    Papers

    Generative Model

  • Cooperative Training of Descriptor and Generator Networks - IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. [All Versions].
  • Apr 14th

    Papers

    Explainable Deep Learning

  • Individual differences among deep neural network models - Nature Communications, 2020. [All Versions].
  • Papers

    Visual Complexity

  • Image complexity and spatial information - International Workshop on Quality of Multimedia Experience, 2013. [All Versions].
  • Apr 13th

    Papers

    Human-Machine Comparison

  • Humans can decipher adversarial images - Nature Communications. 2019. [All Versions].
  • Papers

    Scaling Up Behavioral Studies

  • Exploring human cognition using large image databases - Topics in Cognitive Sciences, 2016. [All Versions].
  • Apr 8th

    Papers

    Question Answering

  • Do People Ask Good Questions? - Computational Brain & Behavior, 2018. [All Versions].
  • Asking goal-oriented questions and learning from answers - CogSci'19, 2019. [All Versions].
  • Papers

    Scaling Up Behavioral Studies

  • Machine-generated theories of human decision-making - Science, 2021. [All Versions].
  • Integrating explanation and prediction in computational social science - Nature, 2021. [All Versions].
  • Mar 24th

    Papers

    Pragmatics

  • Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding - Transactions of the Association for Computational Linguistics, 2017. [All Versions].
  • Pragmatically Informative Image Captioning with Character-Level Inference - NAACL'18, 2018. [All Versions].
  • Academic Tools

    Paper Writing

  • The Machine Learning Reproducibility Checklist - McGill University. Guidelines for introducing a machine learning algorithm with guarantee of reproducibility.
  • How to construct a Nature summary paragraph - Nature. Nature official guidelines for composing abstracts.
  • Academic Tools

    Courses

  • Computational Cognitive Science Courses - MIT, Harvard, Stanford. Courses on computational cognitive science from MIT, Harvard, Stanford.
  • Academic Tools

    Programming

  • Probabilistic Models of Cognition - MIT. The probabilistic approach to cognitive science, which models learning and reasoning as inference in complex probabilistic models.
  • Mar 22nd

    Papers

    AI Commonsense Reasoning

  • Broaden the Vision: Geo-Diverse Visual Commonsense Reasoning - EMNLP'21, 2021. [All Versions].
  • Mar 17th

    Mar 16th

    Academic Tools

    Paper Reading

  • How to navigate a scientific paper with time constraints: a graphics approach - MIT. MIT guidance on strategies for reading papers given different time constraints.
  • How to (seriously) read a scientific paper - Science, 2016. [All Versions]. Science interview on reading scientific papers.
  • Academic Tools

    Literature Management

  • Scientific literature: Information overload - Nature, 2016. [All Versions]. Perspective on handling overloaded information from scientific literature.
  • How to keep up with the scientific literature - Science, 2016. Science interview on organizing scientific papers.
  • Academic Tools

    Paper Writing

  • Scientific Papers - Nature. Nature guidance on writing scientific papers.
  • Mar 14th

    Mar 11th

    Papers

    Pragmatics

  • Pragmatic Reasoning through Semantic Inference - Semantics & Pragmatics, 2016. [All Versions].
  • Mar 5th

    Mar 3rd

    Papers

    Cognitive Development

  • Perception of partly occluded objects in infancy - Cognitive Psychology, 1983. [All Versions].
  • Feb 27th

    Papers

    Learning with Cognitive Plausibility

  • Attention over Learned Object Embeddings Enables Complex Visual Reasoning - NIPS'21, 2021. [All Versions].
  • Feb 24th

    Feb 23rd

    Papers

    Causality

  • Do six-month-old infants perceive causality? - Cognition, 1987. [All Versions].
  • Feb 21st

    Papers

    Neural-Symbolic AI

  • Grounded Language Learning Fast and Slow - ICLR'21, 2021. [All Versions]. [Projectstars45].
  • Feb 20th

    Feb 16th

    Papers

    Knowledge Representation

  • Qualitative and quantitative simulation: bridging the gap - Artificial Intelligence, 1997. [All Versions].