Optimal Rates for Generalization of Gradient Descent Methods with Deep Neural Networks
arXiv 2606.06764•aa9cbcf48e27d6a41b00c8dd469298cc953c281c3aa8c5c4f034f8b3fee3bdf3
Lp-momentsalgorithmic-stabilityanomaly-detectioncausal-inferencecounterfactual-generationdeep-learninggeneralization-theorygpu-accelerationgradient-descentkernel-methodsmachine-learningneural-tangent-kernelopen-source-softwarerobustnessstochastic-gradient-descent
Paper metadata
- arXiv ID
- 2606.06764
- 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
- aa9cbcf48e27d6a41b00c8dd469298cc953c281c3aa8c5c4f034f8b3fee3bdf3
- Enrichment time
- 2026-06-08T07:23:56Z
- AI-assisted enrichment
- Yes
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