arXiv Machine Learning By Gal Lifshitz, Shahar Zuler, Ori Fouks, Dan Raviv

L-SR1: Learned Symmetric-Rank-One Preconditioning

Read the original on arXiv Machine Learning →

arXiv:2508. 12270v3 Announce Type: replace Abstract: End-to-end deep learning has achieved impressive results but often relies on large labeled datasets, exhibits limited generalization to unseen scenarios, and incurs substantial computational cost.

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
Jun 19

Deep-Unfolded Coordination

arXiv:2606. 19920v1 Announce Type: cross Abstract: Distributed optimization is a highly scalable and structurally transparent technique to solve multi-agent robotics problems; however, such methods often suffer from the need for highly-specialized, problem-specific hyperparameter tunings.

By Hunter Kuperman, Minchan Jung, Rahul V. Ghosh, Alex Oshin, Evangelos A. Theodorou
arXiv Machine Learning
5d ago

Gradient Surgery for Physics-Informed Neural Networks

The paper introduces PAM-GS, a physics-aware gradient surgery technique for Physics-Informed Neural Networks (PINNs). It addresses the highly imbalanced multi-task optimisation problem in PINNs by adaptively mitigating task interference based on observed gradient conflicts. Experiments on four PDE benchmarks show that PAM-GS achieves competitive solution accuracy while maintaining strong task-balanced performance, outperforming existing methods on most problems.

By Thomas Borsani, Giuseppe Di Fatta
arXiv Computer Vision
Sep 24

Learn2Splat: Extending the Horizon of Learned 3DGS Optimization

arXiv:2605.15760v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) optimization is most commonly performed using general-purpose first-order optimizers such as Adam or SGD. Although rob...

By Naama Pearl, Stefano Esposito, Haofei Xu, Amit Peleg, Patricia Gschossmann, Lorenzo Porzi, Peter Kontschieder, Gerard Pons-Moll, Andreas Geiger