arXiv Machine Learning July 28, 2026 By Lavinia Ghita, Dhruv Desai, Jake Goldberg, Roman Yokunda Enzmann

Low-Rank Dependence Decomposition via Accelerated Symmetric Non-negative Matrix Factorization

Read the original on arXiv Machine Learning →

arXiv:2607. 24518v1 Announce Type: new Abstract: Symmetric non-negative matrix factorization (SymNMF) recovers latent group structure from a dependence matrix, but its dense, quadratic-memory objective has confined prior work to moderate sizes.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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llms rag fine-tuning benchmarks

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