AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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From task structures to world models: what do LLMs know? cell.com
Trends in Cognitive Sciences, 2024. [All Versions]. [Preprint]. In what sense does a large language model (LLM) have knowledge? The authors answer by granting LLMs ‘instrumental knowledge’: knowledge gained by using next-word generation as an instrument. The authors then ask…
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Human-in-the-loop for Bayesian autonomous materials phase mapping cell.com
Matter. [All Versions]. Autonomous experimentation achieves user objectives more efficiently than Edisonian studies by combining machine learning and laboratory automation to iteratively select and perform experiments. Integrating knowledge from theory, simulations, literature…
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Empowering biomedical discovery with AI agents cell.com
Cell, 2024. [All Versions]. The authors envision “AI scientists” as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking…
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Theory-based Bayesian models of inductive learning and reasoning cell.com
Trends in Cognitive Sciences, 2006. [All Versions]. [Preprint]. Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional…
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Synthesizing theories of human language with Bayesian program induction cap.csail.mit.edu
Nature Communications, 2022. [All Versions].
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Building Machines That Learn and Think Like People cambridge.org
Behavioral and Brain Sciences, 2017. [All Versions]. [Preprint]. Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as…
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Organizational Principles of Abstract Words in the Human Brain bilab.bnu.edu.cn
Cerebral Cortex, 2018. [All Versions].
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Levels of Analysis for Machine Learning baicsworkshop.github.io
ICLR'20 Bridging AI and Cognitive Science Workshop, 2020. [All Versions]. A Marr's paradigm account on machine learning.
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Pixelor: A Competitive Sketching AI Agent. So you think you can beat me? ayankumarbhunia.github.io
ACM SIGGRAPH'20, 2020. [All Versions]. [Project]. Rationality in feature sketching.
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Reducing the dimensionality of data with neural networks asset-pdf.scinapse.io
Science, 2006. [All Versions]. The original paper on Variational Autoencoder.
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Automated Reinforcement Learning (AutoRL): A Survey and Open Problems arxiv.org
2022. [All Versions]. A comprehensive review on AutoRL.
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Discovering State and Action Abstractions for Generalized Task and Motion Planning arxiv.org
AAAI'22, 2022. [All Versions].
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Meta-Interpretive Learning as Metarule Specialisation arxiv.org
Machine Learning, 2021. [All Versions].
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An overview of multi-agent reinforcement learning from game theoretical perspective arxiv.org
2020. [All Versions]. Yaodong Yang's review on multi-agent reinforcement learning from the perspective of game theory.
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Abductive Knowledge Induction From Raw Data arxiv.org
IJCAI'21, 2021. [All Versions].
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Grounded Language Learning Fast and Slow arxiv.org
ICLR'21, 2021. [All Versions]. [Project].
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Compositional Generalization via Neural-Symbolic Stack Machines arxiv.org
NeurIPS'20, 2020. [All Versions].
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Learning programs by learning from failures arxiv.org
Machine Learning, 2020. [All Versions].
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An Explicitly Relational Neural Network Architecture arxiv.org
ICML'20, 2020. [All Versions].
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Neural Logic Machines arxiv.org
ICLR'19, 2019. [All Versions].
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Neural Logic Reinforcement Learning arxiv.org
ICML'19, 2019. [All Versions].
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Few-shot Bayesian imitation learning with logical program policies. arxiv.org
AAAI'20, 2020. [All Versions].
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Multi-Agent Cooperation and the Emergence of (Natural) Language arxiv.org
ICLR'17, 2017. [All Versions]. The original paper on the emergence of language in multi-agent reinforcement learning.
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Representation Learning: A Review and New Perspectives arxiv.org
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013. [All Versions]. Yoshua Bengio's review on representation learning.
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War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars arxiv.org
2023. [All Versions].
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The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4 arxiv.org
Microsoft Research AI4Science, 2023. [All Versions]. [Project]. A survey on the performance of LLMs within the context of scientific discovery, focusing on GPT-4.
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ChipNeMo: Domain-Adapted LLMs for Chip Design arxiv.org
2023. [All Versions]. ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design. Instead of directly deploying off-the-shelf commercial or open-source LLMs, the authors instead adopt the following domain adaptation techniques…
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Human Comprehensible Active Learning of Genome-Scale Metabolic Networks arxiv.org
AAAI Spring Symposium Series 2023 on Computational Scientific Discovery, 2023. [All Versions]. [Extended Abstract]. [Slides]. This work introduces a novel machine learning framework ILP-iML1515 based on Inductive Logic Programming (ILP) that performs abductive logical reasoning…
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Large Language Models for Software Engineering: A Systematic Literature Review arxiv.org
2023. [All Versions]. A systematic literature review on LLM4SE, with a particular focus on understanding how LLMs can be exploited to optimize processes and outcomes.
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The SocialAI School: Insights from Developmental Psychology Towards Artificial Socio-Cultural Agents arxiv.org
ICML'23 Workshop on Theory-of-Mind, 2023. [All Versions]. [Project].
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The Quest for a Common Model of the Intelligent Decision Maker arxiv.org
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.
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Program Synthesis with Large Language Models arxiv.org
2021. [All Versions]. This paper explores the limits of the current generation of large language models for program synthesis in general purpose programming languages.
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Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability arxiv.org
NeurIPS'21, 2021. [All Versions]. A formal treatment on the generalization problem in reinforcement learning.
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Automated Biodesign Engineering by Abductive Meta-Interpretive Learning arxiv.org
AAAI Spring Symposium Series 2021 on Artificial Intelligence for Synthetic Biology, 2021. [All Versions]. This work proposes an automated biodesign engineering framework empowered by Abductive Meta-Interpretive Learning (MetaAbd), a novel machine learning approach that combines…
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Inductive logic programming at 30: a new introduction arxiv.org
Journal of Artificial Intelligence Research, 2020. [All Versions]. A 30-year comprehensive review on Inductive Logic Programming.
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Learning Task-General Representations with Generative Neuro-Symbolic Modeling arxiv.org
ICLR'21, 2021. [All Versions].
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Discovering Symbolic Models from Deep Learning with Inductive Biases arxiv.org
NeurIPS'20, 2020. [All Versions].
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Generating new concepts with hybrid neuro-symbolic models. arxiv.org
CogSci'20, 2020. [All Versions].
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X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust arxiv.org
CVPR XAI Workshop'19, 2019. [All Versions]. This work presents a new explainable AI (XAI) framework aimed at increasing justified human trust and reliance in the AI machine through explanations. The authors pose explanation as an iterative communication process, i.e. dialog…
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From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following arxiv.org
ICLR'19, 2019. [All Versions].
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Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review arxiv.org
2018. [All Versions]. [Slides]. Sergey Levine's tutorial on treating reinforcement learning probabilisticly.
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Unsupervised Representaton Learning with Deep Convolutional Generative Adversarial Networks arxiv.org
ICLR'16, 2016. [All Versions].
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On the information bottleneck theory of deep learning artemyk.github.io
Journal of Statistical Mechanics: Theory and Experiment, 2019. [All Versions].
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Experimental Games and Social Decision Making annualreviews.org
Annual Review of Psychology, 2021. [All Versions]. Experimental games model situations in which the future outcomes of individuals and groups depend on their own choices and on those of other (groups of) individuals. Games are a powerful tool to identify the neural and…
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Experiments with More Than One Random Factor: Designs, Analytic Models, and Statistical Power annualreviews.org
Annual Review of Psychology, 2017. [All Versions]. A comprehensive review of the quantitative analysis techniques for behavioral studies.
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Integrated Task and Motion Planning annualreviews.org
Annual Review of Control, Robotics, and Autonomous Systems, 2021. [All Versions]. The problem of planning for a robot that operates in environments containing a large number of objects, taking actions to move itself through the world as well as to change the state of the…
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Logical reduction of metarules andrewcropper.com
Machine Learning, 2019. [All Versions].
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Learning higher-order logic programs andrewcropper.com
Machine Learning, 2019. [All Versions].
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Learning efficient logic programs andrewcropper.com
Machine Learning, 2018. [All Versions].
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Playgol: Learning Programs Through Play andrewcropper.com
IJCAI'19, 2019. [All Versions].
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