1 citations · 1 across the 4 of their papers we have counts for
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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…
HyperHELM: Hyperbolic Hierarchy Encoding for mRNA Language Modeling
Max van Spengler, Artem Moskalev, Tommaso Mansi +2
Language models are increasingly applied to biological sequences like proteins and mRNA, yet their default Euclidean geometry may mismatch the hierarchical structures inherent to b…
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…