Improving Infinitely Deep Bayesian Neural Networks with Nesterov's Accelerated Gradient Method
arXiv 2603.25024•6130fa50dbeb158661777298feb909f8706de0797b810b298b87d2a9f736b617
bayesian-neural-networksbayesian-optimizationbias-mitigationdemographic-paritydistribution-to-distributionefficient-global-optimizationfunctional-tuckergaussian-processgeneralizationkernel-mean-embeddingsmachine-learningmemorization-overparameterization','topological-data-analysis','mixture-density-networksnesterov-accelerated-gradientnfesnugget-jitterpost-processingprobabilistic-forecastingregression-fairnessresidual-as-teacherrkhsrules-and-facts-modelstochastic-differential-equationsstudent-teachertensor-decomposition
Paper metadata
- arXiv ID
- 2603.25024
- 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
- 6130fa50dbeb158661777298feb909f8706de0797b810b298b87d2a9f736b617
- Enrichment time
- 2026-03-27T07:23:53Z
- AI-assisted enrichment
- Yes
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