12 citations · 16 across the 2 of their papers we have counts for
4 papers
Covariance in Physics and Convolutional Neural Networks
Miranda C. N. Cheng, Vassilis Anagiannis, Maurice Weiler +3
In this proceeding we give an overview of the idea of covariance (or equivariance) featured in the recent development of convolutional neural networks (CNNs). We study the similari…
Causal Confusion in Imitation Learning
Pim de Haan, Dinesh Jayaraman, Sergey Levine
Behavioral cloning reduces policy learning to supervised learning by training a discriminative model to predict expert actions given observations. Such discriminative models are no…
Topological Constraints on Homeomorphic Auto-Encoding
Pim de Haan, Luca Falorsi
When doing representation learning on data that lives on a known non-trivial manifold embedded in high dimensional space, it is natural to desire the encoder to be homeomorphic whe…
Explorations in Homeomorphic Variational Auto-Encoding
Luca Falorsi, Pim de Haan, Tim R. Davidson +4
The manifold hypothesis states that many kinds of high-dimensional data are concentrated near a low-dimensional manifold. If the topology of this data manifold is non-trivial, a co…