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On the impact of activation and normalization in obtaining isometric embeddings at initialization
In this paper, we explore the structure of the penultimate Gram matrix in deep neural
networks, which contains the pairwise inner products of outputs corresponding to a batch of …
networks, which contains the pairwise inner products of outputs corresponding to a batch of …
Wasserstein gradient flows of MMD functionals with distance kernel and Cauchy problems on quantile functions
We give a comprehensive description of Wasserstein gradient flows of maximum mean
discrepancy (MMD) functionals $\mathcal F_\nu:=\text {MMD} _K^ 2 (\cdot,\nu) $ towards …
discrepancy (MMD) functionals $\mathcal F_\nu:=\text {MMD} _K^ 2 (\cdot,\nu) $ towards …
Position: -Algebraic Machine Learning Moving in a New Direction
Machine learning has a long collaborative tradition with several fields of mathematics, such
as statistics, probability and linear algebra. We propose a new direction for machine …
as statistics, probability and linear algebra. We propose a new direction for machine …
Analyzing the Geometric Structure of Deep Learning Decision Boundaries
M Geyer - 2023 - search.proquest.com
Training deep learning models is an incredibly effective method for finding function
approximators. However, understanding the behavior of these trained models from a first …
approximators. However, understanding the behavior of these trained models from a first …