10 citations · 11 across the 10 of their papers we have counts for
12 papers · 1 filter
Joint Flow Matching for Generator-Consistent Classification
Hayden McAlister, Lech Szymanski
We introduce Joint Flow Matching (JFM), a training framework for continuous normalising flows over multiple variables. Standard flow matching transports variables from noise to dat…
Classifying States of the Hopfield Network with Improved Accuracy, Generalization, and Interpretability
Hayden McAlister, Anthony Robins, Lech Szymanski
We extend the existing work on Hopfield network state classification, employing more complex models that remain interpretable, such as densely-connected feed-forward deep neural ne…
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…