5 papers · 1 filter
Amortising Inference and Meta-Learning Priors in Neural Networks
Tommy Rochussen, Vincent Fortuin
One of the core facets of Bayesianism is in the updating of prior beliefs in light of new evidenceso how can we maintain a Bayesian approach if we have no prior belief…
Sparse Gaussian Neural Processes
Tommy Rochussen, Vincent Fortuin
Despite significant recent advances in probabilistic meta-learning, it is common for practitioners to avoid using deep learning models due to a comparative lack of interpretability…
Structured Partial Stochasticity in Bayesian Neural Networks
Tommy Rochussen
Bayesian neural network posterior distributions have a great number of modes that correspond to the same network function. The abundance of such modes can make it difficult for app…
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning
Matthew Ashman, Tommy Rochussen, Adrian Weller
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 approxima…
Amortised Inference in Bayesian Neural Networks
Tommy Rochussen
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. Probabilistic meta-learning…