6 citations · 9 across the 4 of their papers we have counts for
5 papers
On the VC dimension of deep group convolutional neural networks
Anna Sepliarskaia, Sophie Langer, Johannes Schmidt-Hieber
We study the generalization capabilities of Group Convolutional Neural Networks (GCNNs) with ReLU activation function by deriving upper and lower bounds for their Vapnik-Chervonenk…
On genuine invariance learning without weight-tying
Artem Moskalev, Anna Sepliarskaia, Erik J. Bekkers +1
In this paper, we investigate properties and limitations of invariance learned by neural networks from the data compared to the genuine invariance achieved through invariant weight…
VC dimensions of group convolutional neural networks
Philipp Christian Petersen, Anna Sepliarskaia
We study the generalization capacity of group convolutional neural networks. We identify precise estimates for the VC dimensions of simple sets of group convolutional neural networ…
LieGG: Studying Learned Lie Group Generators
Artem Moskalev, Anna Sepliarskaia, Ivan Sosnovik +1
Symmetries built into a neural network have appeared to be very beneficial for a wide range of tasks as it saves the data to learn them. We depart from the position that when symme…
How to Not Measure Disentanglement
Anna Sepliarskaia, Julia Kiseleva, Maarten de Rijke
To evaluate disentangled representations several metrics have been proposed. However, theoretical guarantees for conventional metrics of disentanglement are missing. Moreover, conv…