ResNets of All Shapes and Sizes: Convergence of Training Dynamics in the Large-scale Limit
arXiv 2603.18168•080d0c8559d6ccb57f9c1d7a8d32d0edf446902400fee61565ab6e72b2149512
DeepONetMCMCWassersteinbanditscausal-representation-learningclusteringdata-preparationdeep-learningdenoisersdiffusion-modelsempirical-bayesinsurance-AIkernel-methodsmachine-learningneural-operatorsno-u-turn-samplerrecursive-transportresnetsselective-inferencetransformers
Paper metadata
- arXiv ID
- 2603.18168
- 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
- 080d0c8559d6ccb57f9c1d7a8d32d0edf446902400fee61565ab6e72b2149512
- Enrichment time
- 2026-03-20T07:24:08Z
- AI-assisted enrichment
- Yes
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