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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

Introduction to kernel methods Deep kernel learning Deep Gaussian processes Deep kernel processes and deep kernel machines Meta-learning deep kernel Gaussian processes

Part 6 — Priors

Part 7 — Identifiability and symmetries

Part 8 — Scalability

Part 9 — Applications

Part 10 — Topical developments

Part 5 — Kernel methods

Chapter 23. Introduction to kernel methods
Chapter 24. Deep kernel learning
Chapter 25. Deep Gaussian processes
Chapter 26. Deep kernel processes and deep kernel machines
Chapter 27. Meta-learning deep kernel Gaussian processes

PAC-Bayesian deep neural network ensembles

Introduction to kernel methods