An Automatic Learning Rate Schedule Algorithm for Achieving Faster Convergence and Steeper Descent

17 October 2023

Papers citing "An Automatic Learning Rate Schedule Algorithm for Achieving Faster Convergence and Steeper Descent"

8 / 8 papers shown

Title
Theoretical Foundation of Flow-Based Time Series Generation: Provable Approximation, Generalization, and Efficiency Jiangxuan Long Zhao-quan Song Chiwun Yang AI4TS 65 0 0 18 Mar 2025
Theoretical Guarantees for High Order Trajectory Refinement in Generative Flows Chengyue Gong Xiaoyu Li Yingyu Liang Jiangxuan Long Zhenmei Shi Zhao-quan Song Yu Tian 48 3 0 12 Mar 2025
Scaling Law Phenomena Across Regression Paradigms: Multiple and Kernel Approaches Yifang Chen Xuyang Guo Xiaoyu Li Yingyu Liang Zhenmei Shi Zhao-quan Song 41 3 0 03 Mar 2025
$Theoretical Constraints on the Expressive Power of $\mathsf{RoPE}$-based Tensor Attention Transformers$ Theoretical Constraints on the Expressive Power of $\mathsf{RoPE}$ -based Tensor Attention Transformers Xiaoyu Li Yingyu Liang Zhenmei Shi Zhao-quan Song Mingda Wan 43 8 0 23 Dec 2024
Meta-Learning a Cross-lingual Manifold for Semantic Parsing Tom Sherborne Mirella Lapata 38 13 0 26 Sep 2022
Step-size Adaptation Using Exponentiated Gradient Updates Ehsan Amid Rohan Anil Christopher Fifty Manfred K. Warmuth 14 9 0 31 Jan 2022
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly Yuchen Jin Tianyi Zhou Liangyu Zhao Yibo Zhu Chuanxiong Guo Marco Canini Arvind Krishnamurthy 18 18 0 22 May 2021
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 234 11,568 0 09 Mar 2017