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New research analyzes loss landscapes and stability in deep matrix factorization

By PulseAugur Editorial · [3 sources] ·

Two new research papers delve into the theoretical underpinnings of deep matrix factorization (DMF). The first paper provides a comprehensive analysis of the loss landscape for regularized DMF, characterizing critical points and establishing conditions for convergence to local or global minimizers. The second paper examines the stability of low-rank implicit regularization in perturbed DMF, deriving spectral conditions for gradient descent and analyzing the impact of noise on convergence and eigenvalue recovery. AI

RANK_REASON Cluster contains two academic papers on arXiv discussing theoretical aspects of deep matrix factorization.

Read on arXiv cs.LG →

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New research analyzes loss landscapes and stability in deep matrix factorization

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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Po Chen, Rujun Jiang, Peng Wang ·

    A Complete Loss Landscape Analysis of Regularized Deep Matrix Factorization

    arXiv:2506.20344v3 Announce Type: replace-cross Abstract: Despite its wide range of applications across various domains, the optimization foundations of deep matrix factorization (DMF) remain largely open. In this work, we aim to fill this gap by conducting a comprehensive study …

  2. arXiv stat.ML TIER_1 English(EN) · Jingzhe Wang, Hung-Hsu Chou ·

    Implicit Regularization in Perturbed Deep Matrix Factorization: Spectral Conditions and Stability

    arXiv:2605.28613v1 Announce Type: cross Abstract: This paper studies the stability of low-rank implicit regularization in perturbed deep matrix factorization, where the target matrix is corrupted by a noise matrix. We first derive sufficient spectral conditions under which gradie…

  3. arXiv stat.ML TIER_1 English(EN) · Hung-Hsu Chou ·

    Implicit Regularization in Perturbed Deep Matrix Factorization: Spectral Conditions and Stability

    This paper studies the stability of low-rank implicit regularization in perturbed deep matrix factorization, where the target matrix is corrupted by a noise matrix. We first derive sufficient spectral conditions under which gradient descent exhibits a low-rank phase in the noisel…

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