Search Results for author: Jonathan Schwarz

Found 11 papers, 5 papers with code

Powerpropagation: A sparsity inducing weight reparameterisation

2 code implementations NeurIPS 2021 Jonathan Schwarz, Siddhant M. Jayakumar, Razvan Pascanu, Peter E. Latham, Yee Whye Teh

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 the effective scaling up of models.

Behavior Priors for Efficient Reinforcement Learning

no code implementations27 Oct 2020 Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess

In this work we consider how information and architectural constraints can be combined with ideas from the probabilistic modeling literature to learn behavior priors that capture the common movement and interaction patterns that are shared across a set of related tasks or contexts.

Continuous Control Hierarchical Reinforcement Learning +2

Multiplicative Interactions and Where to Find Them

no code implementations ICLR 2020 Siddhant M. Jayakumar, Wojciech M. Czarnecki, Jacob Menick, Jonathan Schwarz, Jack Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu

We explore the role of multiplicative interaction as a unifying framework to describe a range of classical and modern neural network architectural motifs, such as gating, attention layers, hypernetworks, and dynamic convolutions amongst others.

Meta-Learning surrogate models for sequential decision making

no code implementations28 Mar 2019 Alexandre Galashov, Jonathan Schwarz, Hyunjik Kim, Marta Garnelo, David Saxton, Pushmeet Kohli, S. M. Ali Eslami, Yee Whye Teh

We introduce a unified probabilistic framework for solving sequential decision making problems ranging from Bayesian optimisation to contextual bandits and reinforcement learning.

Bayesian Optimisation Decision Making +4

Functional Regularisation for Continual Learning with Gaussian Processes

1 code implementation ICLR 2020 Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh

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.

Bayesian Inference Continual Learning +2

Attentive Neural Processes

6 code implementations ICLR 2019 Hyunjik Kim, andriy mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh

Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions.

Experience Replay for Continual Learning

no code implementations ICLR 2019 David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Greg Wayne

We examine this issue in the context of reinforcement learning, in a setting where an agent is exposed to tasks in a sequence.

Continual Learning

Neural Processes

12 code implementations4 Jul 2018 Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S. M. Ali Eslami, Yee Whye Teh

A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision.

Progress & Compress: A scalable framework for continual learning

no code implementations ICML 2018 Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, Raia Hadsell

This is achieved by training a network with two components: A knowledge base, capable of solving previously encountered problems, which is connected to an active column that is employed to efficiently learn the current task.

Active Learning Atari Games +1

The NarrativeQA Reading Comprehension Challenge

1 code implementation TACL 2018 Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, Edward Grefenstette

Reading comprehension (RC)---in contrast to information retrieval---requires integrating information and reasoning about events, entities, and their relations across a full document.

Ranked #9 on Question Answering on NarrativeQA (BLEU-1 metric)

Information Retrieval Question Answering +1

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