Hugging Face Trending Papers June 4, 2026

$p$-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.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv Machine Learning

Jun 5

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

arXiv:2606. 06117v1 Announce Type: cross Abstract: We introduce pVR, a topological machine learning framework for alignment-free genomic sequence classification that combines $p$-adic numbers with topological data analysis.

By Tirtharaj Dash, Gunja Sachdeva

llms rag benchmarks safety

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

rag

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

arXiv Machine Learning

Jun 5

Symmetric Divergence and Normalized Similarity: A Unified Topological Framework for Representation Analysis

arXiv:2606. 06342v1 Announce Type: cross Abstract: Topological Data Analysis (TDA) offers a principled, intrinsic lens for comparing neural representations.

By Yan Wang, Tianyang Hu

llms benchmarks

arXiv Machine Learning

Jul 22

Topology-Driven Clustering: Enhancing Performance with Betti Number Filtration

arXiv:2505. 04346v2 Announce Type: replace Abstract: Clustering aims at partitioning data points into groups of similar objects without knowing about the class labels.

By Arghya Pratihar, Kushal Bose, Swagatam Das

benchmarks

arXiv Machine Learning

Jul 24

Writhe-Based Polymer Link Classification Using Machine Learning

arXiv:2607. 20657v1 Announce Type: cross Abstract: Unique and rapid classification of knots and links is an open mathematical problem that is relevant to a range of (bio)physical systems, including polymer melts, DNA, and proteins.

By Jack Beda, Djordje Mihajlovic, Kasturi Barkataki, Davide Michieletto

Related stories

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

Learning Topological Representations for Molecular Dynamics

Prototype Selection Using Topological Data Analysis

Symmetric Divergence and Normalized Similarity: A Unified Topological Framework for Representation Analysis

Topology-Driven Clustering: Enhancing Performance with Betti Number Filtration

Writhe-Based Polymer Link Classification Using Machine Learning