3 papers
cs.LG2025
Hyperbolic Genome Embeddings
Raiyan R. Khan, Philippe Chlenski, Itsik Pe'er
Current approaches to genomic sequence modeling often struggle to align the inductive biases of machine learning models with the evolutionarily-informed structure of biological sys…
cs.LG2025
Even Faster Hyperbolic Random Forests: A Beltrami-Klein Wrapper Approach
Philippe Chlenski, Itsik Pe'er
Decision trees and models that use them as primitives are workhorses of machine learning in Euclidean spaces. Recent work has further extended these models to the Lorentz model of…
cs.LG2025
Manify: A Python Library for Learning Non-Euclidean Representations
Philippe Chlenski, Kaizhu Du, Dylan Satow +2
We present Manify, an open-source Python library for non-Euclidean representation learning. Leveraging manifold learning techniques, Manify provides tools for learning embeddings i…