Interpreting FCDNNs via RG on Exponential Family
arXiv 2606.00157•49c0929ff939e1c17f6bbb23bff88e3416ede2cca9801dedf4ea3606a3d2efee
Feynman-KacPINNsRiemannian optimizationSVMU-statisticsclusteringconformal predictiondeep learning interpretabilitydimension reductiondirected graphs testing methodsexponential familyextremesgraph signal processingmachine learningneural Tucker factorizationoperator learningphysics-informed neural networksquantile regressionrenormalization groupreplicabilityrobust statisticssuper-resolutiontensor completiontheoretical MLuncertainty quantification
Paper metadata
- arXiv ID
- 2606.00157
- Version
- Not specified by this published record
- Category
- Statistics — Machine Learning (stat.ML)
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Evidence and limitations
- Source ID
- arxiv_stat_ml
- Record identifier
- 49c0929ff939e1c17f6bbb23bff88e3416ede2cca9801dedf4ea3606a3d2efee
- Enrichment time
- 2026-06-02T07:23:57Z
- AI-assisted enrichment
- Yes
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