AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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Infinite Latent Feature Models and the Indian Buffet Process mlg.eng.cam.ac.uk
Gatsby Computational Neuroscience Unit Technical Report 2005-001, 2005. [All Versions].
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A Relational Approach to Tool-Use Learning in Robots link.springer.com
ILP'12, 2012. [All Versions].
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Abduction in Logic Programming link.springer.com
Computational Logic, 2002. [All Versions]. [Preprint]. Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer…
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Abduction, Induction, and Analogy link.springer.com
Model-Based Reasoning in Science and Technology, 2010. [All Versions]. The distinctions and relations between Abduction, Induction, and Analogy.
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Filters, random fields and maximum entropy (FRAME): Towards a unified theory for texture modeling link.springer.com
International Journal of Computer Vision, 1998. [All Versions]. [Preprint]. This article presents a statistical theory for texture modeling. This theory combines filtering theory and Markov random field modeling through the maximum entropy principle, and interprets and…
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Do People Ask Good Questions? link.springer.com
Computational Brain & Behavior, 2018. [All Versions].
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From intention to implementation: automating biomedical research via LLMs link.springer.com
Science China Information Sciences, 2025. [All Versions]. Conventional biomedical research is increasingly labor-intensive due to the exponential growth of scientific literature and datasets. Artificial intelligence (AI), particularly large language models (LLMs), has the…
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Abductive learning: towards bridging machine learning and logical reasoning link.springer.com
Science China Information Sciences, 2019. [All Versions].
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Structured Generative Models for Scene Understanding link.springer.com
International Journal of Computer Vision, 2025. [All Versions]. This position paper argues for the use of structured generative models (SGMs) for the understanding of static scenes. This requires the reconstruction of a 3D scene from an input image (or a set of multi-view…
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Configurable 3D Scene Synthesis and 2D Image Rendering with Per-pixel Ground Truth Using Stochastic Grammars link.springer.com
International Journal of Computer Vision, 2018. [All Versions]. [Preprint]. This work proposes a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground…
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Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework link.springer.com
Machine Learning, 2022. [All Versions]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.
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Scenic: a language for scenario specification and data generation link.springer.com
Machine Learning, 2022. [All Versions]. This paper proposes a domain-specific language, Scenic, for describing scenarios that are distributions over scenes and the behaviors of their agents over time. Scenic combines concise, readable syntax for spatiotemporal relationships…
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Meta-Interpretive Learning from noisy images link.springer.com
Machine Learning, 2018. [All Versions].
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Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP link.springer.com
Machine Learning, 2018. [All Versions]. During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not…
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Learning to perceive and act by trial and error link.springer.com
Machine Learning, 1991. [All Versions].
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Beneficial and harmful explanatory machine learning link.springer.com
Machine Learning, 2021. [All Versions]. Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong…
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Individual differences among deep neural network models kriegeskortelab.zuckermaninstitute.columbia.edu
Nature Communications, 2020. [All Versions].
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Human-level control through deep reinforcement learning klab.tch.harvard.edu
Nature, 2015. [All Versions]. The original paper on solving Atari games via Deep Q-Network.
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Idiosyncratic Tower of Babel: Individual differences in word-meaning representation increase as word abstractness… journals.sagepub.com
Psychological Science, 2021. [All Versions]. [All Versions]. Humans primarily rely on language to communicate, on the basis of a shared understanding of the basic building blocks of communication: words. Do we mean the same things when we use the same words? Although cognitive…
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Expert Tool Users Show Increased Differentiation between Visual Representations of Hands and Tools jneurosci.org
Journal of Neuroscience, 2021. [All Versions].
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A Survey of Preference-Based Reinforcement Learning Methods jmlr.org
Journal of Machine Learning Research, 2017. [All Versions].
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Analysis of Langevin Monte Carlo via Convex Optimization jmlr.org
Journal of Machine Learning Research, 2019. [All Versions]. This paper provides new insights on the Unadjusted Langevin Algorithm. The authors show that this method can be formulated as the first order optimization algorithm for an objective functional defined on the…
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The Indian Buffet Process: An Introduction and Review jmlr.org
Journal of Machine Learning Research, 2011. [All Versions]. The Indian buffet process is a stochastic process defining a probability distribution over equivalence classes of sparse binary matrices with a finite number of rows and an unbounded number of columns. This…
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Adapting Behavior via Intrinsic Reward: A Survey and Empirical Study jair.org
Journal of Artificial Intelligence Research, 2020. [All Versions].
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From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning jair.org
Journal of Artificial Intelligence Research, 2018. [All Versions]. This work considers the problem of constructing abstract representations for planning in high-dimensional, continuous environments. The authors assume an agent equipped with a collection of high-level actions…
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Learning Explanatory Rules from Noisy Data jair.org
Journal of Artificial Intelligence Research, 2018. [All Versions]. The original paper for differential Inductive Logic Programming.
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Reinforcement learning: A survey jair.org
Journal of Artificial Intelligence Research, 1996. [All Versions]. Leslie Kaelbling's review on reinforcement learning.
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Deep Forest: Towards An Alternative to Deep Neural Networks ijcai.org
IJCAI'17, 2017. [All Versions]. [Project]. This paper proposes gcForest, a decision tree ensemble approach with performance highly competitive to deep neural networks in a broad range of tasks. In contrast to deep neural networks which require great effort in hyper-parameter…
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Bayesian Inverse Reinforcement Learning ijcai.org
IJCAI'07, 2007. [All Versions]. A Bayesian account on classic inverse reinforcement learning.
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Neural Task Programming: Learning to Generalize Across Hierarchical Tasks ieeexplore.ieee.org
ICRA'18, 2018. [All Versions].
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Detecting Unseen Visual Relations Using Analogies ieeexplore.ieee.org
CVPR'19, 2019. [All Versions].
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Network dissection: Quantifying interpretability of deep visual representations ieeexplore.ieee.org
CVPR'17, 2017. [All Versions]. [Project]. [Dataset: Places365]. The original paper on visualizing the class activation maps to explain convolutional neural networks.
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Data-Efficient Learning for Complex and Real-Time Physical Problem Solving Using Augmented Simulation ieeexplore.ieee.org
Robotics and Automation Letters, 2021. [All Versions].
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Semi-Supervised Abductive Learning and Its Application to Theft Judicial Sentencing ieeexplore.ieee.org
ICDM'20, 2020. [All Versions]. [Preprint]. In many practical tasks, there are usually two kinds of common information: cheap unlabeled data and domain knowledge in the form of symbols. There are some attempts using one single information source, such as semi-supervised learning…
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Cooperative Training of Descriptor and Generator Networks ieeexplore.ieee.org
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. [All Versions]. This paper studies the cooperative training of two generative models for image modeling and synthesis. Both models are parametrized by convolutional neural networks (ConvNets). The first model…
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Taking the Human Out of the Loop: A Review of Bayesian Optimization ieeexplore.ieee.org
Proceedings of the IEEE, 2015. [All Versions]. [Preprint]. Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such…
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JARVIS-1: Open-World Multi-Task Agents With Memory-Augmented Multimodal Language Models ieeexplore.ieee.org
IEEE Transactions on Pattern Analysis and Machine Intelligence. [All Versions]. Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon…
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LogiCode: An LLM-Driven Framework for Logical Anomaly Detection ieeexplore.ieee.org
IEEE Transactions on Automation Science and Engineering, 2024. [All Versions]. This paper presents LogiCode, a novel framework that leverages Large Language Models (LLMs) for identifying logical anomalies in industrial settings, moving beyond the traditional focus on structural…
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Deep Learning: How the Mind Overrides Experience hk1lib.org
Cambridge University Press, 2011. [All Versions].
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Multi-task reinforcement learning in humans gershmanlab.com
Nature Human Behavior, 2021. [All Versions].
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The successor representation in human reinforcement learning gershmanlab.com
Nature Human Behavior, 2017. [All Versions].
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Galactica: A Large Language Model for Science galactica.org
Meta AI, 2022. [All Versions]. A large language model trained on large-scale scientific corpus.
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Generalized Inverse Planning: Learning Lifted non-Markovian Utility for Generalizable Task Representation export.arxiv.org
2020. [All Versions].
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Semantic features of object concepts generated with GPT-3 escholarship.org
CogSci'22, 2022. [All Versions]. Semantic features have been playing a central role in investigating the nature of our conceptual representations. Yet the enormous time and effort required to empirically sample and norm features from human raters has restricted their use to a…
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Evaluating and Modeling Social Intelligence: A Comparative Study of Human and AI Capabilities escholarship.org
CogSci'24, 2024. [All Versions]. This work eveloped a comprehensive theoretical framework for social dynamics and introduced two evaluation tasks: Inverse Reasoning (IR) and Inverse Inverse Planning (IIP). The approach also encompassed a computational model based on recursive…
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Robotic search for optimal cell culture in regenerative medicine elifesciences.org
eLife, 2022. [All Versions]. This paper develops a robotic AI system with a batch Bayesian optimization algorithm that autonomously induces the differentiation of induced pluripotent stem cell-derived retinal pigment epithelial (iPSC-RPE) cells. From 200 million possible…
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Value signals guide abstraction during learning elifesciences.org
eLife, 2022. [All Versions].
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Meta-interpretive learning: application to grammatical inference doc.ic.ac.uk
Machine Learning, 2014. [All Versions]. Stephen Muggleton's original paper on Meta-Interpretive Learning (MIL).
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The Seven Tools of Causal Inference, with Reflections on Machine Learning dl.acm.org
Communications of the ACM, 2019. [All Versions]. Judea Pearl's review on causal inference in probabilistic graph models.
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Apprenticeship Learning via Inverse Reinforcement Learning dl.acm.org
ICML'04, 2004. [All Versions]. Pieter Abbeel and Andrew Ng's original paper on inverse reinforcement learning (IRL).
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