AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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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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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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