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Predicting the outputs of finite deep neural networks trained with noisy gradients

Physical review. E(2020)10.1103/physreve.104.064301Source: DataRank Database

Predicting the outputs of finite deep neural networks trained with noisy gradients is a research paper published in Physical review. E (2020). On theSindex it has a DataRank of 0. It has been cited 22 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access22 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (5 grants)Impact (FWCI 0.13)OA: greenTopics (36)IDs (PubMed, OpenAlex)

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 (4)

Ben-David, Oded,Sompolinsky, Haim,Ringel, Zohar,Naveh, Gadi

Live enrichment

PubMed:35030925 OpenAlex:https://openalex.org/W3204621306

National Institutes of Health

Grant: 1U19NS104653

Israel Science Foundation

Grant: 2250/19

National Institutes of Health

Grant: 1U19NS104653-01

Sensorimotor processing, decision making, and internal states: towards a realistic multiscale circuit model of the larval zebrafish brain

Swartz Foundation

Gatsby Charitable Foundation

FWCI

0.13

Citation Percentile

0.6%

Citation Trend

2023

greenLicense: APS Licenses for Journal Article Re-use

repositoryhttps://arxiv.org/pdf/2004.01190.pdf

publisherhttps://link.aps.org/accepted/10.1103/PhysRevE.104.064301

publisherhttps://link.aps.org/article/10.1103/PhysRevE.104.064301

publisherhttp://harvest.aps.org/v2/journals/articles/10.1103/PhysRevE.104.064301/fulltext

Fields of Study

Gaussian Processes and Bayesian InferenceAdversarial Robustness in Machine LearningMachine Learning and Data Classification02 engineering and technology0202 electrical engineering, electronic engineering, information engineering

Keywords

Convolutional neural networkArtificial neural networkGaussian processGaussianLimit (mathematics)AlgorithmGaussian blurComputer scienceTangentNoise (video)Gaussian functionMathematicsApplied mathematicsArtificial intelligencePhysicsImage (mathematics)Mathematical analysisImage processingGeometryFOS: Computer and information sciencesComputer Science - Machine LearningStatistical Mechanics (cond-mat.stat-mech)Computer Science - Neural and Evolutionary ComputingFOS: Physical sciencesMachine Learning (stat.ML)Disordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksMachine Learning (cs.LG)Statistics - Machine LearningNeural and Evolutionary Computing (cs.NE)Condensed Matter - Statistical Mechanics