Parabolic Approximation Line Search for DNNs

28 March 2019

Papers citing "Parabolic Approximation Line Search for DNNs"

33 / 33 papers shown

Title
Using a one dimensional parabolic model of the full-batch loss to estimate learning rates during training Max Mutschler Kevin Laube A. Zell ODL 17 1 0 31 Aug 2021
KOALA: A Kalman Optimization Algorithm with Loss Adaptivity A. Davtyan Sepehr Sameni L. Cerkezi Givi Meishvili Adam Bielski Paolo Favaro ODL 93 2 0 07 Jul 2021
Empirically explaining SGD from a line search perspective Max Mutschler A. Zell ODL LRM 36 4 0 31 Mar 2021
Empirical study towards understanding line search approximations for training neural networks Younghwan Chae D. Wilke 100 11 0 15 Sep 2019
Automatic and Simultaneous Adjustment of Learning Rate and Momentum for Stochastic Gradient Descent Tomer Lancewicki Selçuk Köprü 19 5 0 20 Aug 2019
On the Variance of the Adaptive Learning Rate and Beyond Liyuan Liu Haoming Jiang Pengcheng He Weizhu Chen Xiaodong Liu Jianfeng Gao Jiawei Han ODL 152 1,894 0 08 Aug 2019
Training Neural Networks for and by Interpolation Leonard Berrada Andrew Zisserman M. P. Kumar 3DH 27 62 0 13 Jun 2019
Large Scale Structure of Neural Network Loss Landscapes Stanislav Fort Stanislaw Jastrzebski 41 83 0 11 Jun 2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Mingxing Tan Quoc V. Le 3DV MedIm 109 17,950 0 28 May 2019
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates Sharan Vaswani Aaron Mishkin I. Laradji Mark Schmidt Gauthier Gidel Simon Lacoste-Julien ODL 72 208 0 24 May 2019
On the Convergence of Adam and Beyond Sashank J. Reddi Satyen Kale Surinder Kumar 55 2,482 0 19 Apr 2019
Gradient-only line searches: An Alternative to Probabilistic Line Searches D. Kafka D. Wilke ODL 52 14 0 22 Mar 2019
Adaptive Gradient Methods with Dynamic Bound of Learning Rate Liangchen Luo Yuanhao Xiong Yan Liu Xu Sun ODL 46 600 0 26 Feb 2019
Asymmetric Valleys: Beyond Sharp and Flat Local Minima Haowei He Gao Huang Yang Yuan ODL MLT 54 149 0 02 Feb 2019
Averaging Weights Leads to Wider Optima and Better Generalization Pavel Izmailov Dmitrii Podoprikhin T. Garipov Dmitry Vetrov A. Wilson FedML MoMe 93 1,643 0 14 Mar 2018
A Walk with SGD Chen Xing Devansh Arpit Christos Tsirigotis Yoshua Bengio 68 118 0 24 Feb 2018
L4: Practical loss-based stepsize adaptation for deep learning Michal Rolínek Georg Martius ODL 81 64 0 14 Feb 2018
ShakeDrop Regularization for Deep Residual Learning Yoshihiro Yamada Masakazu Iwamura Takuya Akiba K. Kise 55 162 0 07 Feb 2018
MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler Andrew G. Howard Menglong Zhu A. Zhmoginov Liang-Chieh Chen 148 19,124 0 13 Jan 2018
Visualizing the Loss Landscape of Neural Nets Hao Li Zheng Xu Gavin Taylor Christoph Studer Tom Goldstein 234 1,873 0 28 Dec 2017
Practical Gauss-Newton Optimisation for Deep Learning Aleksandar Botev H. Ritter David Barber ODL 31 228 0 12 Jun 2017
Shake-Shake regularization Xavier Gastaldi 3DPC BDL OOD 60 380 0 21 May 2017
Online Learning Rate Adaptation with Hypergradient Descent A. G. Baydin R. Cornish David Martínez-Rubio Mark Schmidt Frank Wood ODL 49 247 0 14 Mar 2017
Big Batch SGD: Automated Inference using Adaptive Batch Sizes Soham De A. Yadav David Jacobs Tom Goldstein ODL 120 62 0 18 Oct 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 631 36,599 0 25 Aug 2016
Neither Quick Nor Proper -- Evaluation of QuickProp for Learning Deep Neural Networks C. Brust Sven Sickert Marcel Simon E. Rodner Joachim Denzler SSeg VLM 11 3 0 14 Jun 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.4K 192,638 0 10 Dec 2015
Optimizing Neural Networks with Kronecker-factored Approximate Curvature James Martens Roger C. Grosse ODL 69 999 0 19 Mar 2015
Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci Philipp Hennig ODL 47 126 0 10 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 842 149,474 0 22 Dec 2014
Qualitatively characterizing neural network optimization problems Ian Goodfellow Oriol Vinyals Andrew M. Saxe ODL 83 519 0 19 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 943 99,991 0 04 Sep 2014
ADADELTA: An Adaptive Learning Rate Method Matthew D. Zeiler ODL 113 6,619 0 22 Dec 2012