AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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nvim-mini/mini.nvim#mini.extra github.com
Module of mini.nvim with extra functionality for its modules. Contains 20+ 'mini.pick' pickers, 'mini.ai' textobjects, and more.
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nvim-mini/mini.nvim#mini.ai github.com
Module of mini.nvim for extending and creating a/i textobjects. It enhances some built-in textobjects, creates extensive set of new ones (like a*, a<Space>, a?, and more), and allows user to create their own (via Lua patterns or functions). Supports dot-repeat, different search…
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milanglacier/yarepl.nvim#aider-extensions github.com
Integration with aider-chat, a TUI AI coding assistant.
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TensorFlow Snippets marketplace.visualstudio.com
This extension includes a set of useful code snippets for developing TensorFlow models in Visual Studio Code.
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Visual Studio IntelliCode marketplace.visualstudio.com
This extension provides AI-assisted development features including autocomplete and other insights based on understanding your code context.
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Understanding Tools: Task-Oriented Object Modeling, Learning and Recognition yzhu.io
CVPR'15, 2015. [All Versions]. [Project]. The original paper introducing affordance and physically-grounded tool use into computer vision.
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A Theoretical Computer Science Perspective on Consciousness worldscientific.com
Journal of Artificial Intelligence and Consciousness, 2020. [All Versions].
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Visual Search at Pinterest web.archive.org
KDD'15, 2015. [All Versions]. Large scale user study in the development of the recommendations system by Pinterest.
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Leveraging Language for Abstraction and Program Search vigilworkshop.github.io
ICML'20, 2020. [All Versions].
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The order effect in human abductive reasoning: an empirical and computational study tandfonline.com
Journal of Experimental & Theoretical Artificial Intelligence, 2006. [All Versions].
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Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others static1.squarespace.com
NeurIPS'21, 2021. [All Versions].
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Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies soft-computing.de
IEEE Transactions on Systems, Man, and Cybernetics, 2008. [All Versions]. A comprehensive review on the application of pareto optimality to multiobjective machine learning.
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CX-ToM: Counterfactual explanations with theory-of-mind for enhancing human trust in image recognition models sciencedirect.com
iScience, 2022. [All Versions]. This work proposes CX-ToM, short for counterfactual explanations with theory-of-mind, a new explainable AI (XAI) framework for explaining decisions made by a deep convolutional neural network (CNN). In contrast to the current methods in XAI that…
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Exploration: from machines to humans sciencedirect.com
Current Opinion in Behavioral Sciences, 2020. [All Versions].
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Tasks for aligning human and machine planning sciencedirect.com
Current Opinion in Behavioral Sciences, 2019. [All Versions].
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Theory of mind as inverse reinforcement learning sciencedirect.com
Current Opinion in Behavioral Sciences, 2019. [All Versions]. This paper reviews the idea that Theory of Mind --- humans' ability to reason about other people's mental states --- can be formalized as inverse reinforcement learning. Under this framework, expectations about how…
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The secret life of predictive brains: what's spontaneous activity for? sciencedirect.com
Trends in Cognitive Sciences, 2021. [All Versions]. A neuroscience account on brain as a generative model.
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Intuitive Physics: Current Research and Controversies sciencedirect.com
Trends in Cognitive Sciences, 2018. [All Versions]. Hongjing Lu's review on intuitive physics.
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The neural bases of complex tool use in humans sciencedirect.com
Trends in Cognitive Sciences, 2004. [All Versions]. A neuroscience account of human tool use.
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Intensive tool-practice and skillfulness facilitate the extension of body representations in humans sciencedirect.com
Neuropsychologia, 2014. [All Versions].
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Latent Semantic Indexing: A Probabilistic Analysis sciencedirect.com
Journal of Computer and System Sciences, 2000. [All Versions]. The original paper on hierarchical topic model.
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Action Understanding as Inverse Planning sciencedirect.com
Cognition, 2009. [All Versions]. [Appendix]. The original paper on Inverse Planning, a computational implementation of Theory of Mind. Humans are adept at inferring the mental states underlying other agents’ actions, such as goals, beliefs, desires, emotions and other thoughts…
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Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning sciencedirect.com
Artificial Intelligence, 1999. [All Versions]. The original paper on operation reinforcement learning.
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Qualitative and quantitative simulation: bridging the gap sciencedirect.com
Artificial Intelligence, 1997. [All Versions]. Shortcomings of qualitative simulation and of quantitative simulation motivate combining them to do simulations exhibiting strengths of both. The resulting class of techniques is called semiquantitative simulation. One approach to…
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Making sense of sensory input sciencedirect.com
Artificial Intelligence, 2021. [All Versions].
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Analogy between concepts sciencedirect.com
Artificial Intelligence, 2019. [All Versions]. A mathematical account on analogy.
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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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