Selecting and Composing Learning Rate Policies for Deep Neural Networks

Selecting and Composing Learning Rate Policies for Deep Neural Networks

24 October 2022

Ling Liu

Papers citing "Selecting and Composing Learning Rate Policies for Deep Neural Networks"

8 / 8 papers shown

Title
HyperbolicLR: Epoch insensitive learning rate scheduler Tae-Geun Kim 47 2 0 21 Jul 2024
On the Efficiency of Privacy Attacks in Federated Learning Nawrin Tabassum Ka-Ho Chow Xuyu Wang Wenbin Zhang Yanzhao Wu FedML 42 1 0 15 Apr 2024
Guidelines for the Regularization of Gammas in Batch Normalization for Deep Residual Networks Bum Jun Kim Hyeyeon Choi Hyeonah Jang Dong Gu Lee Wonseok Jeong Sang Woo Kim 24 4 0 15 May 2022
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly Yuchen Jin Dinesh Manocha Liangyu Zhao Yibo Zhu Chuanxiong Guo Marco Canini Arvind Krishnamurthy 39 18 0 22 May 2021
Forward and Reverse Gradient-Based Hyperparameter Optimization Luca Franceschi Michele Donini P. Frasconi Massimiliano Pontil 133 409 0 06 Mar 2017
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 274 5,331 0 05 Nov 2016
The Effects of Hyperparameters on SGD Training of Neural Networks Thomas Breuel 72 63 0 12 Aug 2015
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 313 39,252 0 01 Sep 2014