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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 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.
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.
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.
