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DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations

(2022)10.1145/3514094.3534148Source: DataRank Database

DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations is a research paper (2022). On theSindex it has a DataRank of 0. It has been cited 29 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access29 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (10 grants)OA: goldTopics (11)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 (5)

Paul Pu Liang ORCID,Zihao Deng ORCID,Ruslan Salakhutdinov ORCID,Louis–Philippe Morency ORCID,Yiwei Lyu ORCID

Live enrichment

PubMed:19910364

National Science Foundation

Grant: 1722822 and 1750439

NSF (National Science Foundation)

Grant: IIS1763562

ONR Grant

Grant: N000141812861

National Institutes of Health

Grant: #R01MH125740 and #R01MH096951

National Science Foundation

Grant: 1750439

CAREER: Learning Nonverbal Signatures

National Institutes of Health

Grant: 5R01MH125740-03

SCH: INT: Collaborative Research: Context-Adaptive Multimodal Informatics for Psychiatric Discharge Planning

National Institutes of Health

Grant: 1R01MH096951-01

Automated Facial Expression Analysis for Research and Clinical Use

National Science Foundation

Grant: 1722822

SCH: INT: Collaborative Research: Dyadic Behavior Informatics for Psychotherapy Process and Outcome

National Science Foundation

Grant: 1763562

AF: RI: Medium: Collaborative Research: Understanding and Improving Optimization in Deep and Recurrent Networks

National Institutes of Health

Grant: 5U01MH116925-04

Robust Predictors of Mania and Psychosis

goldLicense: cc-by

https://dl.acm.org/doi/pdf/10.1145/3514094.3534148

publisherhttps://dl.acm.org/doi/10.1145/3514094.3534148

https://doi.org/10.1145/3514094.3534148

https://dx.doi.org/10.48550/arxiv.2203.02013

http://arxiv.org/abs/2203.02013

Fields of Study

0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

FOS: Computer and information sciencesComputer Science - Machine LearningComputer Science - Computation and LanguageArtificial Intelligence (cs.AI)Computer Science - Artificial IntelligenceComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionComputation and Language (cs.CL)Machine Learning (cs.LG)