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Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie Published: 19 Dec 2019, Last Modified: 05.
Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie Published: 19 Dec 2019, Last Modified: 05.
ABSTRACT We provide a theoretical explanation for the effectiveness of gradient clipping in training deep neural networks. The key ingredient is a new smoothness condition derived.
Abstract: The success of the Adam optimizer on a wide array of architectures has made it the default in settings where stochastic gradient descent (SGD) performs poorly. However, our.
Understanding the Context
We introduce EPISODE, an algorithm for federated learning with heterogeneous data under the relaxed smoothness setting for training deep neural networks, and provide state-of-the-art.
Convergence of AdaGrad for non-convex objectives: Simple proofs and relaxed assumptions. In Conference on Learning Theory, 2023. -Jing Zhao Zhang, Tian Xing He, Suvrit Sra, and Ali.
Why gradient clipping accelerates training: A theoretical justification for adaptivity. arXiv preprint arXiv:1905.11881 (2019) [2] Riabinin et al. Gluon: Making Muon & Scion Great Again!
NeurIPS, 2020. [2] Zhang, Jingzhao, et al. Why are adaptive methods good for attention models? NeurIPS, 2020. [3] Zhang, Jingzhao, et al. Why gradient clipping accelerates training: A.
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Key Insights
While relaxed smoothness has been extensively explored in recent yearsparticularly in centralized settings for training neural network modelsthis paper advances the study to.
We propose to clamp the norm of the logit output, which can enhance the noise-robostness of existing loss functions with theoretical guarantee.
069 We then consider stochastic generalized-smooth nonconvex optimization, for which we propose a novel 070 Independently-Normalized Stochastic Gradient Descent (I-NSGD) algorithm. Specifically, I.
