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On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis

arXiv (Cornell University)(2024)10.48550/arxiv.2402.04520Source: DataRank Database

On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis is a research paper published in arXiv (Cornell University) (2024). On theSindex it has a DataRank of 0.

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (2 grants)OA: greenTopics (21)IDs (OpenAlex)

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Open Access

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Reusable (0/3)

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Authors (4)

Thomas W. Lin,Zhao Song ORCID,Han Liu ORCID,Jerry Yao-Chieh Hu

Live enrichment

OpenAlex:https://openalex.org/W4391671224

National Science Foundation

Grant: 1841569

CAREER: An Integrated Inferential Framework for Big Data Research and Education

National Science Foundation

Grant: 1740735

Collaborative Research: TRIPODS Institute for Optimization and Learning

greenLicense: cc-by-nc-sa

repositoryhttps://arxiv.org/pdf/2402.04520

repositoryhttp://arxiv.org/abs/2402.04520

repositoryhttps://doi.org/10.48550/arxiv.2402.04520

https://dx.doi.org/10.48550/arxiv.2402.04520

https://proceedings.mlr.press/v235/hu24j.html

Fields of Study

Theoretical and Computational PhysicsStatistical Mechanics and EntropyMachine Learning (cs.LG)Artificial Intelligence (cs.AI)Machine Learning (stat.ML)FOS: Computer and information sciences0102 computer and information sciences01 natural sciences

Keywords

Computer scienceComputational complexity theoryAlgorithmApplied mathematicsMathematical optimizationMathematicsFOS: Computer and information sciencesComputer Science - Machine LearningArtificial Intelligence (cs.AI)Computer Science - Artificial IntelligenceStatistics - Machine LearningMachine Learning (stat.ML)Machine Learning (cs.LG)