157 citations · 325 across the 19 of their papers we have counts for
19 papers
Intervention Complexity as a Canonical Reward and a Measure of Intelligence
Brendan McCane
The Legg--Hutter universal intelligence measure provides a rigorous scalar assessment of general intelligence as expected reward across all computable environments, weighted by sim…
Learning to Explore by Reinforcement over High-Level Options
Liu Juncheng, McCane Brendan, Mills Steven
Autonomous 3D environment exploration is a fundamental task for various applications such as navigation. The goal of exploration is to investigate a new environment and build its o…
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…
RocNet: Recursive Octree Network for Efficient 3D Deep Representation
Juncheng Liu, Steven Mills, Brendan McCane
We introduce a deep recursive octree network for the compression of 3D voxel data. Our network compresses a voxel grid of any size down to a very small latent space in an autoencod…
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…