10 citations · 11 across the 7 of their papers we have counts for
10 papers · 1 filter
Conceptual capacity and effective complexity of neural networks
Lech Szymanski, Brendan McCane, Craig Atkinson
We propose a complexity measure of a neural network mapping function based on the diversity of the set of tangent spaces from different inputs. Treating each tangent space as a lin…
MIME: Mutual Information Minimisation Exploration
Haitao Xu, Brendan McCane, Lech Szymanski +1
We show that reinforcement learning agents that learn by surprise (surprisal) get stuck at abrupt environmental transition boundaries because these transitions are difficult to lea…
GRIm-RePR: Prioritising Generating Important Features for Pseudo-Rehearsal
Craig Atkinson, Brendan McCane, Lech Szymanski +1
Pseudo-rehearsal allows neural networks to learn a sequence of tasks without forgetting how to perform in earlier tasks. Preventing forgetting is achieved by introducing a generati…
VASE: Variational Assorted Surprise Exploration for Reinforcement Learning
Haitao Xu, Brendan McCane, Lech Szymanski
Exploration in environments with continuous control and sparse rewards remains a key challenge in reinforcement learning (RL). Recently, surprise has been used as an intrinsic rewa…
Switched linear projections for neural network interpretability
Lech Szymanski, Brendan McCane, Craig Atkinson
We introduce switched linear projections for expressing the activity of a neuron in a deep neural network in terms of a single linear projection in the input space. The method work…
Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting
Craig Atkinson, Brendan McCane, Lech Szymanski +1
Neural networks can achieve excellent results in a wide variety of applications. However, when they attempt to sequentially learn, they tend to learn the new task while catastrophi…