research
∙
03/13/2021
Conceptual capacity and effective complexity of neural networks
We propose a complexity measure of a neural network mapping function bas...
share
research
∙
01/16/2020
MIME: Mutual Information Minimisation Exploration
We show that reinforcement learning agents that learn by surprise (surpr...
research
∙
11/27/2019
GRIm-RePR: Prioritising Generating Important Features for Pseudo-Rehearsal
Pseudo-rehearsal allows neural networks to learn a sequence of tasks wit...
research
∙
10/31/2019
VASE: Variational Assorted Surprise Exploration for Reinforcement Learning
Exploration in environments with continuous control and sparse rewards r...
research
∙
09/25/2019
Switched linear projections and inactive state sensitivity for deep neural network interpretability
We introduce switched linear projections for expressing the activity of ...
research
∙
12/06/2018
Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting
Neural networks can achieve extraordinary results on a wide variety of t...
research
∙
06/06/2018
The effect of the choice of neural network depth and breadth on the size of its hypothesis space
We show that the number of unique function mappings in a neural network ...
research
∙
03/08/2018
Some Approximation Bounds for Deep Networks
In this paper we introduce new bounds on the approximation of functions ...
research
∙
02/12/2018
Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep Neural Networks
In general, neural networks are not currently capable of learning tasks ...
research
∙
02/25/2016
