Search Results for author: Max van Spengler

Found 4 papers, 4 papers with code

HypLL: The Hyperbolic Learning Library

1 code implementation9 Jun 2023 Max van Spengler, Philipp Wirth, Pascal Mettes

Deep learning in hyperbolic space is quickly gaining traction in the fields of machine learning, multimedia, and computer vision.

Poincaré ResNet

1 code implementation24 Mar 2023 Max van Spengler, Erwin Berkhout, Pascal Mettes

This paper introduces an end-to-end residual network that operates entirely on the Poincar\'e ball model of hyperbolic space.

Poincare ResNet

1 code implementation ICCV 2023 Max van Spengler, Erwin Berkhout, Pascal Mettes

(iii) Due to the many intermediate operations in Poincare layers, the computation graphs of deep learning libraries blow up, limiting our ability to train on deep hyperbolic networks.

Maximum Class Separation as Inductive Bias in One Matrix

1 code implementation17 Jun 2022 Tejaswi Kasarla, Gertjan J. Burghouts, Max van Spengler, Elise van der Pol, Rita Cucchiara, Pascal Mettes

This paper proposes a simple alternative: encoding maximum separation as an inductive bias in the network by adding one fixed matrix multiplication before computing the softmax activations.

Inductive Bias Long-tail Learning +3

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