284 citations · 1.8k across the 21 of their papers we have counts for
4 papers · 1 filter
Representation learning for improved interpretability and classification accuracy of clinical factors from EEG
Garrett Honke, Irina Higgins, Nina Thigpen +6
Despite extensive standardization, diagnostic interviews for mental health disorders encompass substantial subjective judgment. Previous studies have demonstrated that EEG-based ne…
Representation Matters: Improving Perception and Exploration for Robotics
Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck +8
Projecting high-dimensional environment observations into lower-dimensional structured representations can considerably improve data-efficiency for reinforcement learning in domain…
Unsupervised deep learning identifies semantic disentanglement in single inferotemporal neurons
Irina Higgins, Le Chang, Victoria Langston +4
Deep supervised neural networks trained to classify objects have emerged as popular models of computation in the primate ventral stream. These models represent information with a h…
Disentangling by Subspace Diffusion
David Pfau, Irina Higgins, Aleksandar Botev +1
We present a novel nonparametric algorithm for symmetry-based disentangling of data manifolds, the Geometric Manifold Component Estimator (GEOMANCER). GEOMANCER provides a partial…