AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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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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On the Effectiveness of Large Language Models in Domain-Specific Code Generation dl.acm.org
ACM Transactions on Software Engineering and Methodology, 2025. [All Versions]. Large language models (LLMs) such as ChatGPT have shown remarkable capabilities in code generation. Despite significant achievements, they rely on enormous training data to acquire a broad spectrum…
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ACM Transactions on Software Engineering and Methodology, 2024. [All Versions]. Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents to software engineering researchers the six key challenges that a domain…
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"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output dl.acm.org
CHI EA'24, 2024. [All Versions]. [Preprint]. Large language models can produce creative and diverse responses. However, to integrate them into current developer workflows, it is essential to constrain their outputs to follow specific formats or standards. This work surveyed 51…
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Automatic curriculum learning for deep RL: a short survey dl.acm.org
IJCAI'20, 2020. [All Versions].
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Infinite Hidden Relational Models dl.acm.org
UAI'06, 2006. [All Versions]. [Preprint]. Relational learning analyzes the probabilistic constraints between the attributes of entities and relationships. This work extends the expressiveness of relational models by introducing for each entity (or object) an…
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Nonparametric Bayesian Logic dl.acm.org
UAI'05, 2005. [All Versions]. [Preprint]. The Bayesian Logic (BLOG) language was recently developed for defining first-order probability models over worlds with unknown numbers of objects. It handles important problems in AI, including data association and population…
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What's the Game, then? Opportunities and Challenges for Runtime Behavior Generation dl.acm.org
UIST'24, 2024. [All Versions]. Procedural content generation (PCG), the process of algorithmically creating game components instead of manually, has been a common tool of game development for decades. Recent advances in large language models (LLMs) enable the generation of game…
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CoLadder: Manipulating Code Generation via Multi-Level Blocks dl.acm.org
UIST'24, 2024. [All Versions]. This paper adopted an iterative design process to gain insights into programmers’ strategies when using LLMs for programming. The authors proposed CoLadder, a novel system that supports programmers by facilitating hierarchical task decomposition…
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ACM SIGGRAPH Asia'23, 2023. [All Versions]. By continually observing their environment and communicating, AI agents trained in the Chinese dictionary and the Nüshu corpus collaborate towards creating a standard writing system to encode Chinese.
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A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges dl.acm.org
ICSE'24, 2024. [All Versions]. A survey finding that developers are most motivated to use AI programming assistants because they help developers reduce key-strokes, finish programming tasks quickly, and recall syntax, but resonate less with using them to help brainstorm…
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A Computational Inflection for Scientific Discovery dl.acm.org
Communications of the ACM, 2023. [All Versions].
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Synthesis of Incremental Linear Algebra Programs dl.acm.org
ACM Transactions on Database Systems, 2020. [All Versions]. This article targets the Incremental View Maintenance (IVM) of sophisticated analytics (such as statistical models, machine learning programs, and graph algorithms) expressed as linear algebra programs. This work…
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pix2code: Generating Code from a Graphical User Interface Screenshot dl.acm.org
ACM SIGCHI Symposium on Engineering Interactive Computing Systems, 2018. [All Versions]. [Code]. [Website]. This paper shows that deep learning methods can be leveraged to train a model end-to-end to automatically reverse engineer user interfaces and generate code from a single…
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Zoom In: An Introduction to Circuits distill.pub
Distill, 2020. [All Versions]. A perspective on treating neural networks as circuits.
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Science of Science ctbergstrom.com
Science, 2018. [All Versions]. A comprehensive large-scale review on the science of science.
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Commonsense reasoning and commonsense knowledge in artificial intelligence cs.wellesley.edu
Communications of the ACM, 2015. [All Versions]. Gary Marcus's review on commonsense knowledge in AI.
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Combining Logical Abduction and Statistical Induction: Discovering Written Primitives with Human Knowledge cs.nju.edu.cn
AAAI'17, 2017. [All Versions].
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Visual Analogy: Deep Learning Versus Compositional Models cs.jhu.edu
CogSci'21, 2021. [All Versions]. A human-deep-learning comparison on similarity judgement.
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Learning Part-Based Abstractions for Visual Object Concepts cogtoolslab.github.io
CogSci'21, 2021. [All Versions].
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Abstract strategy learning underlies flexible transfer in physical problem solving cognitivesciencesociety.org
CogSci'20, 2020. [All Versions].
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Cognitive Science as a Source of Forward and Inverse Models of Human Decisions for Robotics and Control cocosci.princeton.edu
Annual Review of Control, Robotics, and Autonomous Systems, 2022. [All Versions]. The review focuses on how cognitive science can provide forward models of human decision-making and inverse models of how humans think about others’ decision-making. The authors highlight relevant…
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Using large-scale experiments and machine learning to discover theories of human decision-making cocosci.princeton.edu
Science, 2021. [All Versions]. A piece of evidence for the merits brought by large-scale behavioral studies in social science.
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Exploring human cognition using large image databases cocosci.princeton.edu
Topics in Cognitive Sciences, 2016. [All Versions].
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Hybrid computing using a neural network with dynamic external memory clgiles.ist.psu.edu
Nature, 2016. [All Versions].
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Human-like property induction is a challenge for large language models charleskemp.com
CogSci'22, 2022.
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Understanding Human Intelligence through Human Limitations cell.com
Trends in Cognitive Sciences, 2020. [All Versions]. [Preprint]. Recent progress in artificial intelligence provides the opportunity to ask the question of what is unique about human intelligence, but with a new comparison class. The author argues that we can understand human…
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