10 citations · 12 across the 8 of their papers we have counts for
5 papers · 1 filter
Fast and Geometrically Grounded Lorentz Neural Networks
Robert van der Klis, Ricardo Chávez Torres, Max van Spengler +3
Hyperbolic space is quickly gaining traction as a promising geometry for hierarchical and robust representation learning. A core open challenge is the development of a mathematical…
Low-distortion and GPU-compatible Tree Embeddings in Hyperbolic Space
Max van Spengler, Pascal Mettes
Embedding tree-like data, from hierarchies to ontologies and taxonomies, forms a well-studied problem for representing knowledge across many domains. Hyperbolic geometry provides a…
Adversarial Attacks on Hyperbolic Networks
Max van Spengler, Jan Zahálka, Pascal Mettes
As hyperbolic deep learning grows in popularity, so does the need for adversarial robustness in the context of such a non-Euclidean geometry. To this end, this paper proposes hyper…
Maximally Separated Active Learning
Tejaswi Kasarla, Abhishek Jha, Faye Tervoort +2
Active Learning aims to optimize performance while minimizing annotation costs by selecting the most informative samples from an unlabelled pool. Traditional uncertainty sampling o…
Maximum Class Separation as Inductive Bias in One Matrix
Tejaswi Kasarla, Gertjan J. Burghouts, Max van Spengler +3
Maximizing the separation between classes constitutes a well-known inductive bias in machine learning and a pillar of many traditional algorithms. By default, deep networks are not…