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Found 5 papers, 5 papers with code
Hypernetwork approach to Bayesian MAML
Unfortunately, previous modifications of MAML are limited due to the simplicity of Gaussian posteriors, MAML-like gradient-based weight updates, or by the same structure enforced for universal and adapted weights.
Reliable Categorical Variational Inference with Mixture of Discrete Normalizing Flows
Variational approximations are increasingly based on gradient-based optimization of expectations estimated by sampling.
Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching
The behavior of many Bayesian models used in machine learning critically depends on the choice of prior distributions, controlled by some hyperparameters that are typically selected by Bayesian optimization or cross-validation.
Correcting Predictions for Approximate Bayesian Inference
Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications.
Variational Bayesian Decision-making for Continuous Utilities
Bayesian decision theory outlines a rigorous framework for making optimal decisions based on maximizing expected utility over a model posterior.
