arXiv:2609.36692v1 Announce Type: cross
Abstract: Matrix optimizers have emerged as a promising direction, with Muon standing out as a prominent design. Revisiting Muon through its full-Gram represen...
By Zixuan Gong, Zeyu Gan, Jiaye Teng, Yong Liu
arXiv:2508. 21022v3 Announce Type: replace Abstract: Subsampled natural gradient descent (SNG) has been used to enable high-precision scientific machine learning, but standard analyses based on stochastic preconditioning fail to provide insight into realistic small-sample settings.
By Gil Goldshlager, Jiang Hu, Lin Lin
arXiv:2609.38095v1 Announce Type: new
Abstract: Backpropagation (BP) dominates deep learning but imposes a massive memory tax. For example, training OPT-30B with Adam requires $\approx$ 600GB of GPU...
By Francois Chaubard, Mykel J. Kochenderfer, Chris R\'e
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
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: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