arXiv Machine Learning By Alejandro Calatrava, Xu Zhang, Ren Wang

SAM-on-the-Curve: Sharpness-Aware Mode Connectivity for Robust Weight-Space Interpolation

Read the original on arXiv Machine Learning →

The paper introduces Sharp Mode Connectivity (SMC), a new formulation of mode connectivity that optimizes for flatness in the entire local neighborhood of a weight-space path rather than just along the path itself. By applying a sharpness-aware approximation to a minimax objective, SMC produces low-loss curves that remain robust to distribution shifts, achieving significant accuracy gains on CIFAR‑10‑C and outperforming standard mode connectivity on several architectures.

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 arXiv Machine Learning.

arXiv Machine Learning
Aug 27

Robust CurveMoE: Multi-Norm Adversarial Defense for Mixture-of-Experts Models via Mode Connectivity

Robust CurveMoE is a mixture‑of‑experts framework that protects neural networks against perturbations defined by multiple norm constraints. It connects norm‑specialized models through a low‑loss path, selectively expertises only influential layers, and shares the rest of the parameters across routing paths. The method introduces contribution‑guided partial updating to reduce curve‑construction cost and provides a theoretical bound on the objective gap between partial and full optimization, achieving consistent improvements in clean, norm‑specific, and Union accuracy on CIFAR‑100 and ImageNet‑100.

By Xu Zhang, Ren Wang
arXiv Computer Vision
Sep 15

Sparsity-Adaptive Sharpness-Aware Minimization

The paper introduces Sparsity-Adaptive Sharpness-Aware Minimization (SA‑SAM), a method that adjusts the perturbation radius in sharpness-aware training to remain consistent as model sparsity increases. It also evaluates a Magnitude‑Weighted Hessian (MWH) importance metric derived from second‑order analysis. Experiments on CIFAR‑10‑C, CIFAR‑100‑C, and ImageNet‑100‑C show that SA‑SAM improves corruption robustness at 80–90% sparsity while maintaining clean accuracy, and the study reports inference throughput at deployment‑relevant sparsity levels.

By Shiryu Ueno, Yoshikazu Hayashi, Kunihito Kato
arXiv Machine Learning
Jul 10

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks

arXiv:2607. 07745v1 Announce Type: new Abstract: While accuracy, robustness, and calibration are all essential for reliable neural networks, they are often studied separately; developing models that satisfy all three simultaneously remains a central challenge.

By Arthur Chiron (IRIT, EPE UT), Franck Mamalet (IRIT, DTIPG - SNCF, UT3), Thomas Massena (IRIT, DTIPG - SNCF, UT3), Thomas Deltort (IRIT), Mathieu Serrurier (IRIT, UT2J)