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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

Part 8 — Scalability

Scalable BDL by single forward pass inference Scalable Gaussian processes Last-layer inference Scalable Laplace approximations Bayesian compression: with quantization Learned Compression with Stochastic Codes

Part 9 — Applications

Part 10 — Topical developments

Part 8 — Scalability

Chapter 37. Scalable BDL by single forward pass inference
Chapter 38. Scalable Gaussian processes
Chapter 39. Last-layer inference
Chapter 40. Scalable Laplace approximations
Chapter 41. Learned Compression with Deterministic Codes
Chapter 42. Learned Compression with Stochastic Codes

Bayesian mixture of experts in BDL

Scalable BDL by single forward pass inference