Bayesian Inference and Deep Learning: Theory and Practice of Generative Models

IntroductionPurpose of this bookThis book depicts the path from Bayesian inference to deep learning as a single long-form technical volume. There is one central theme: how can we handle uncertainty in ...
Nature

Variational Inference Techniques in Bayesian Models

Variational inference is a family of optimisation-based methods for approximating complex posterior distributions in Bayesian models. By transforming inference into an optimisation problem, these ...

Towards Reinforcement Learning-based Flow Space Optimization

Towards Reinforcement Learning-based Flow Space OptimizationA Deep Paradigm Shift in Modern Bayesian Inference — From the Limits of MCMC/Variational Inference to Neural Processes and GFlowNetsIntroduc ...