arXiv:2607. 06976v1 Announce Type: cross Abstract: In this paper, we propose an efficient hybrid least squares/gradient descent (LSGD) method for MIONets to accelerate training.
By Jun Choi, Chang-Ock Lee, Minam Moon
arXiv:2407. 02362v3 Announce Type: replace-cross Abstract: Modern deep neural networks heavily rely on a large number of multiply-accumulate operations, which constitute the predominant computational cost.
By Xuqi Zhu, Huaizhi Zhang, JunKyu Lee, Jiacheng Zhu, Chandrajit Pal, Sangeet Saha, Klaus D. McDonald-Maier, Xiaojun Zhai
arXiv:2512. 17473v3 Announce Type: replace-cross Abstract: We present an algorithm based on the alternating direction method of multipliers (ADMM) for solving nonlinear matrix decompositions (NMD).
By Atharva Awari, Nicolas Gillis, Arnaud Vandaele
arXiv:2608. 10351v1 Announce Type: new Abstract: In this work we present a method to accelerate the optimization of learning high dimensional functions using deep neural network (DNN).
By Karl Pierce, Yuehaw Khoo, Haizhao Yang
arXiv:2608. 14492v1 Announce Type: new Abstract: The Muon optimizer shows clear benefits versus alternatives when pretraining neural networks.
By Ben Anson, Conor Houghton, Edward Milsom
arXiv:2606. 02328v1 Announce Type: new Abstract: We explore Riemannian optimization techniques for rank-factored matrix parameters, targeting contemporary deep learning applications.
By Nicholas Knight