Search Results for author:
Found 7 papers, 1 papers with code
Conceptual capacity and effective complexity of neural networks
We propose a complexity measure of a neural network mapping function based on the diversity of the set of tangent spaces from different inputs.
MIME: Mutual Information Minimisation Exploration
We show that reinforcement learning agents that learn by surprise (surprisal) get stuck at abrupt environmental transition boundaries because these transitions are difficult to learn.
GRIm-RePR: Prioritising Generating Important Features for Pseudo-Rehearsal
Pseudo-rehearsal allows neural networks to learn a sequence of tasks without forgetting how to perform in earlier tasks.
Switched linear projections for neural network interpretability
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.
Switched linear projections and inactive state sensitivity for deep neural network interpretability
The method works by isolating the active subnetwork, a series of linear transformations, that completely determine the entire computation of the deep network for a given input instance.
Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting
We propose a model that overcomes catastrophic forgetting in sequential reinforcement learning by combining ideas from continual learning in both the image classification domain and the reinforcement learning domain.
Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep Neural Networks
In general, neural networks are not currently capable of learning tasks in a sequential fashion.
