Knowledge Tree
AAAI Conference on Artificial Intelligence
Also known as: AAAI conference, 38th Annual AAAI Conference on Artificial Intelligence, Twenty-ninth AAAI Conference on Artificial Intelligence
Facts (22)
Sources
Papers - Dr Vaishak Belle vaishakbelle.github.io 8 facts
referenceVaishak Belle authored the paper 'Component Caching in Hybrid Domains with Piecewise Polynomial Densities', which was published in the proceedings of the 2016 AAAI Conference on Artificial Intelligence.
referenceAnton Dries, Angelika Kimmig, Jesse Davis, Vaishak Belle, and Luc De Raedt authored the paper 'The Symbolic Interior Point Method', which was published in the proceedings of the 2017 AAAI Conference on Artificial Intelligence.
referenceDavide Nitti, Vaishak Belle, Tinne De Laet, and Luc De Raedt authored the paper 'A First-Order Logic of Probability and Only Knowing in Unbounded Domains', which was published in the proceedings of the 2016 AAAI Conference on Artificial Intelligence.
referenceV. Belle and H. J. Levesque authored 'PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains', which was presented at the Cognitive Robotics Workshop in 2014 and the AAAI conference in 2014.
referenceVaishak Belle authored the paper 'Open-Universe Weighted Model Counting', which was published in the proceedings of the 2017 AAAI Conference on Artificial Intelligence.
referenceVaishak Belle and Gerhard Lakemeyer authored the paper 'Planning Over Multi-Agent Epistemic States: A Classical Planning Approach', which was published in the proceedings of the 2015 AAAI Conference on Artificial Intelligence.
referenceVaishak Belle, Guy Van den Broeck, and Andrea Passerini authored the paper 'Satisfiability and Model Counting in Open Universes', which was published in the 2016 Beyond NP Workshop at the AAAI Conference on Artificial Intelligence.
referenceC. J. Muise, S. A. McIlraith, and V. Belle authored 'Computing Contingent Plans via Fully Observable Non-Deterministic Planning', which was presented at the ICAPS Workshop: Models and Paradigms for Planning under Uncertainty in 2014 and the AAAI conference in 2014.
Knowledge Graphs: Opportunities and Challenges - Springer Nature link.springer.com Apr 3, 2023 5 facts
referenceLin Y, Liu Z, Sun M et al. published 'Learning entity and relation embeddings for knowledge graph completion' in the proceedings of the Twenty-ninth AAAI Conference on Artificial Intelligence in 2015.
referenceMinervini P, Bošnjak M, Rocktäschel T et al. published 'Differentiable reasoning on large knowledge bases and natural language' in the proceedings of the AAAI Conference on Artificial Intelligence in 2020.
referenceMessner J, Abboud R, Ceylan II published 'Temporal knowledge graph completion using box embeddings' in the proceedings of the AAAI Conference on Artificial Intelligence in 2022.
claimWang Z, Zhang J, Feng J et al published the paper 'Knowledge graph embedding by translating on hyperplanes' in the Proceedings of the AAAI Conference on Artificial Intelligence in 2014.
claimWang X, Wang D, Xu C et al published the paper 'Explainable reasoning over knowledge graphs for recommendation' in the Proceedings of the AAAI Conference on Artificial Intelligence in 2019.
A Survey on the Theory and Mechanism of Large Language Models arxiv.org Mar 12, 2026 4 facts
referenceThe paper 'Assessing pre-trained models for transfer learning through distribution of spectral components' was published in the Proceedings of the AAAI Conference on Artificial Intelligence (pp. 22560–22568) and is cited in section 4.2.1 of 'A Survey on the Theory and Mechanism of Large Language Models'.
referenceThe paper 'A stochastic approach to bi-level optimization for hyperparameter optimization and meta learning' was published in the Proceedings of the AAAI Conference on Artificial Intelligence, pages 17913–17920, and is cited in section 4.3.1 of 'A Survey on the Theory and Mechanism of Large Language Models'.
referenceThe paper 'Align-pro: a principled approach to prompt optimization for llm alignment' was published in the Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39, pp. 27653–27661.
referenceThe paper 'Unleashing the potential of large language models as prompt optimizers: analogical analysis with gradient-based model optimizers' was published in the Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39, pp. 25264–25272.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 4 facts
referenceLiu et al. (2024) introduced a method for continual knowledge graph embedding using incremental distillation in 'Towards continual knowledge graph embedding via incremental distillation', published in the Proceedings of the AAAI Conference on Artificial Intelligence.
referenceThe paper 'DKPLM: decomposable knowledge-enhanced pre-trained language model for natural language understanding' was published in the Proceedings of the AAAI Conference on Artificial Intelligence in 2022.
referenceMa et al. (2024) introduced 'Star', a method for boosting low-resource information extraction by structure-to-text data generation with large language models, in the Proceedings of the AAAI Conference on Artificial Intelligence.
referenceLiu et al. (2020) introduced 'K-BERT', a method for enabling language representation with knowledge graphs, in the Proceedings of the AAAI Conference on Artificial Intelligence.
Understanding LLM Understanding skywritingspress.ca Jun 14, 2024 1 fact
referenceManas, O., Krojer, B., and Agrawal, A. published 'Improving Automatic VQA Evaluation Using Large Language Models' in the 38th Annual AAAI Conference on Artificial Intelligence in 2024.