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Handbook of Bayesian Deep Learning

Handbook of Bayesian Deep Learning

Front matter

Part 1 — Sampling methods

Part 2 — Laplace approximations

Part 3 — Variational inference

Part 4 — Ensemble methods

Part 5 — Kernel methods

Part 6 — Priors

Part 7 — Identifiability and symmetries

Symmetries in Bayesian neural networks Symmetries and sampling Learning inductive bias and symmetries Interpretable and identifiable deep generative models via Bayesian pyramids and their variants Bayesian mixture of experts in BDL

Part 8 — Scalability

Part 9 — Applications

Part 10 — Topical developments

Interpretable and identifiable deep generative models via Bayesian pyramids and their variants

Chapter migration pending.

Learning inductive bias and symmetries

Bayesian mixture of experts in BDL