10 citations · 11 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
PProCRC: Probabilistic Collaboration of Image Patches
Tapabrata Chakraborti, Brendan McCane, Steven Mills +1
We present a conditional probabilistic framework for collaborative representation of image patches. It incorporates background compensation and outlier patch suppression into the m…
CoCoNet: A Collaborative Convolutional Network
Tapabrata Chakraborti, Brendan McCane, Steven Mills +1
We present an end-to-end deep network for fine-grained visual categorization called Collaborative Convolutional Network (CoCoNet). The network uses a collaborative layer after the…