Increasing the accuracy of convolutional neural networks with progressive reinitialisation. - Craig Atkinson; Brendan McCane; Lech Szymanski (IVCNZ, 2017)

Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep Neural Networks. - Craig Atkinson; Brendan McCane; Lech Szymanski; Anthony V. Robins (CoRR, 2018)

Switched linear projections and inactive state sensitivity for deep neural network interpretability. - Lech Szymanski; Brendan McCane; Craig Atkinson (CoRR, 2019)

GRIm-RePR: Prioritising Generating Important Features for Pseudo-Rehearsal. - Craig Atkinson; Brendan McCane; Lech Szymanski; Anthony V. Robins (CoRR, 2019)

MIME: Mutual Information Minimisation Exploration. - Haitao Xu; Brendan McCane; Lech Szymanski; Craig Atkinson (CoRR, 2020)

Conceptual capacity and effective complexity of neural networks. - Lech Szymanski; Brendan McCane; Craig Atkinson (CoRR, 2021)

Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting. - Craig Atkinson; Brendan McCane; Lech Szymanski; Anthony V. Robins (Neurocomputing, 2021)

Conceptual complexity of neural networks. - Lech Szymanski; Brendan McCane; Craig Atkinson (Neurocomputing, 2022)