AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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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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Learning Higher-Order Logic Programs through Abstraction and Invention andrewcropper.com
IJCAI'16, 2016. [All Versions].
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Learning Efficient Logical Robot Strategies Involving Composable Objects andrewcropper.com
IJCAI'15, 2015. [All Versions].
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Forgetting to Learn Logic Programs andrewcropper.com
AAAI'20, 2020. [All Versions].
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Reinforcement learning: An introduction andrew.cmu.edu
MIT Press, 2018. [All Versions]. Richard Sutton's comprehensive book on reinforcement learning.
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SciBERT: A Pretrained Language Model for Scientific Text aclanthology.org
EMNLP'19, 2019. [All Versions]. Searching and synthesizing scientific literature, aiding researchers in hypothesis generation, and assisting with experimental design, making scientific knowledge more accessible.
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Do Llamas Work in English? On the Latent Language of Multilingual Transformers aclanthology.org
ACL'24, 2024. [All Versions]. A preliminary work empirically showing that the intermediate embeddings of multilingual Transformers (1) start far away from output token embeddings; (2) already allow for decoding a semantically correct next token in the middle layers, but give…
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BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology aclanthology.org
EMNLP'23, 2023. [All Versions]. [Project]. This paper presents an automatic evaluation framework for the task of planning experimental protocols, and introduces BioProt: a dataset of biology protocols with corresponding pseudocode representations.
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Large Language Models Meet NL2Code: A Survey aclanthology.org
ACL'23, 2023. [All Versions]. [NL2Code Website]. A paper presenting a comprehensive survey of 27 existing large language models for NL2Code, and also review benchmarks and metrics, suggesting that the key factors contributing to the success of large language models for NL2Code…
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PersLEARN: Research Training through the Lens of Perspective Cultivation aclanthology.org
ACL'23, 2023. [All Versions]. Scientific research is inherently shaped by its authors’ perspectives, influenced by various factors such as their personality, community, or society. Junior researchers often face challenges in identifying the perspectives reflected in the…
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Unit Testing for Concepts in Neural Networks aclanthology.org
Transactions of the Association for Computational Linguistics, 2022. [All Versions]. Testing the concept representation by neural networks through Fodor's theory of concepts.
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ESRA: Explainable Scientific Research Assistant aclanthology.org
ACL'21 Demo Track, 2021. [All Versions]. A tool for constructing and visualizing the knowledge graph of a query keyword in literature retrieving.
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages aclanthology.org
EMNLP'20, 2020. [All Versions]. Completing code, generating programming documentation, and providing technical support, making programming knowledge more accessible to non-experts.
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Experience Grounds Language aclanthology.org
EMNLP'20, 2020. [All Versions]. A perspective on the furture of computational linguistics research---commonsense-driven and embodied language.
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How large language models need symbolism academic.oup.com
National Science Review, 2025. [All Versions]. Advances in artificial intelligence (AI), particularly large language models (LLMs), have achieved remarkable success. This progress stems from ‘scaling laws’---performance improves with greater computation, data and model size…
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Theory of Minds: Understanding Behavior in Groups through Inverse Planning aaai.org
AAAI'19, 2019. [All Versions]. Towards the goal of building machine-learning algorithms with human-like social intelligence, this paper develops a generative model of multiagent action understanding based on a novel representation for these latent relationships called…
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An Approach to Abductive Reasoning in Equational Logic aaai.org
IJCAI'13, 2013. [All Versions]. Abduction has been extensively studied in propositional logic because of its many applications in artificial intelligence. However, its intrinsic complexity has been a limitation to the implementation of abductive reasoning tools in more…
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Learning Systems of Concepts with an Infinite Relational Model aaai.org
AAAI'06, 2006. [All Versions].
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The Public Acquisition of Commonsense Knowledge aaai.org
Proceedings of AAAI Spring Symposium on Acquiring (and Using) Linguistic (and World) Knowledge for Information Access, 2002. [All Versions]. The first attempt for acquring commonsense knowlege from humans' activities on the internet.
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Handbook of Knowledge Representation 1lib.net
Elsevier, 2008. [All Versions]. A pragmatical handbook for all kinds of knowledge representation modes.
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Stealing Machine Learning Models via Prediction APIs usenix.org
(Usenix Security) (Code) Aims at extracting machine learning models in use by remote services.
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Remote Explainability faces the bouncer problem rdcu.be
(Nature Machine Intelligence volume 2, pages529–539) (Code) Shows the impossibility (with one request) or the difficulty to spot lies on the explanations of a remote AI decision.
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Identifying the Machine Learning Family from Black-Box Models rd.springer.com
(CAEPIA) Determines which kind of machine learning model is behind the returned predictions.
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Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis proceedings.neurips.cc
(NeurIPS) Sobol indices provide an efficient way to capture higher-order interactions between image regions and their contributions to a (black box) neural network’s prediction through the lens of variance.
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Stealing the Decoding Algorithms of Language Models people.cs.umass.edu
(CCS) Steal the type and hyperparameters of the decoding algorithms of a LLM.
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