1 code implementation • 9 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.
1 code implementation • 24 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.
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.
1 code implementation • 17 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.