Hugging Face Trending Papers

$p$-adic Bi-Filtrations for Topological Machine Learning on Genomic Sequences

Read the original on Hugging Face Trending Papers →

We introduce pVR, a topological machine learning framework for alignment-free genomic sequence classification that combines $p$-adic numbers with topological data analysis. Each DNA sequence is encoded along two complementary axes: a $p$-adic distance on $k$-mer prefixes, which captures hierarchical positional structure, and a compositional $L_1$ distance on $k$-mer frequencies, which captures local sequence content.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

arXiv Machine Learning
Sep 11

When do cheap embeddings beat protein language models? A theoretically-grounded hashing sketch for biological sequence classification

The paper introduces Murmur2Vec, a lightweight, alignment‑free embedding that uses k‑mer counts hashed with MurmurHash to create a compact representation for biological sequences. It provides a full theoretical analysis, including bias/variance formulas, a Johnson–Lindenstrauss‑style concentration bound, and an excess‑risk bound that clarifies the trade‑off between hash‑table size and classifier performance. Empirically, Murmur2Vec matches or surpasses a fine‑tuned 650M‑parameter ESM‑2 protein language model across several classification tasks, including SARS‑CoV‑2 spike lineage and HIV‑1 Env subtype identification.

By Sarwan Ali, Taslim Murad, Imdadullah Khan, Safi Faizullah
arXiv Machine Learning
Jun 16

Learning Topological Representations for Molecular Dynamics

arXiv:2606. 14737v1 Announce Type: cross Abstract: Molecular dynamics (MD) simulations generate trajectories in a high-dimensional configuration space whose analysis critically depends on molecular descriptors, typically handcrafted observables or learned kinetic embeddings.

By Dominik Geng, Florian Graf, Martin Uray, Roland Kwitt
arXiv Machine Learning
Jun 2

Prototype Selection Using Topological Data Analysis

arXiv:2511. 04873v2 Announce Type: replace-cross Abstract: Prototype selection methods compress a training set, but the existing taxonomy of condensation, edition, hybrid, competence-based, optimization-based, and clustering-based families does not include methods that operate on the multi-scale topological structure of the data.

By Jordan Eckert, Elvan Ceyhan, Henry Schenck