Search Results for author:
Found 3 papers, 1 papers with code
Structured Partial Stochasticity in Bayesian Neural Networks
Bayesian neural network posterior distributions have a great number of modes that correspond to the same network function.
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning
The global inducing point variational approximation for BNNs is based on using a set of inducing inputs to construct a series of conditional distributions that accurately approximate the conditionals of the true posterior distribution.
Amortised Inference in Bayesian Neural Networks
Meta-learning is a framework in which machine learning models train over a set of datasets in order to produce predictions on new datasets at test time.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
