27 citations · 82 across the 5 of their papers we have counts for
4 papers · 1 filter
Powerpropagation: A sparsity inducing weight reparameterisation
Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu +2
The training of sparse neural networks is becoming an increasingly important tool for reducing the computational footprint of models at training and evaluation, as well enabling th…
Meta-Learning surrogate models for sequential decision making
Alexandre Galashov, Jonathan Schwarz, Hyunjik Kim +5
We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning.…
Functional Regularisation for Continual Learning with Gaussian Processes
Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews +2
We introduce a framework for Continual Learning (CL) based on Bayesian inference over the function space rather than the parameters of a deep neural network. This method, referred…
Progress & Compress: A scalable framework for continual learning
Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki +4
We introduce a conceptually simple and scalable framework for continual learning domains where tasks are learned sequentially. Our method is constant in the number of parameters an…