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Simple Hardware-Efficient Long Convolutions for Sequence Modeling

arXiv (Cornell University)(2023)10.48550/arxiv.2302.06646Source: DataRank Database

Simple Hardware-Efficient Long Convolutions for Sequence Modeling is a research paper published in arXiv (Cornell University) (2023). On theSindex it has a DataRank of 0. It has been cited 6 times.

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Open Access6 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (4 grants)Topics (9)IDs (PubMed)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (8)

Elliot L. Epstein,Éric Nguyen ORCID,Armin W. Thomas ORCID,Michael Zhang ORCID,Tri Dao

Live enrichment

PubMed:30945415

National Institutes of Health

Grant: 5U54EB020405-03

Mobility Data Integration to Insight

National Science Foundation

Grant: 1937301

RTML: Large: Continuous Adaptation for Decision Streams

National Science Foundation

Grant: 1763481

AF: Medium: Collaborative Research: Beyond Sparsity: Refined Measures of Complexity for Linear Algebra

National Science Foundation

Grant: 1763315

AF: Medium: Collaborative Research: Beyond Sparsity: Refined Measures of Complexity for Linear Algebra

Fields of Study

Machine Learning (cs.LG)FOS: Computer and information sciences0202 electrical engineering, electronic engineering, information engineering02 engineering and technology01 natural sciences0105 earth and related environmental sciences

Keywords

FOS: Computer and information sciencesComputer Science - Machine LearningMachine Learning (cs.LG)