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Augment your batch: better training with larger batches

Augment your batch: better training with larger batches

27 January 2019
Elad Hoffer
Tal Ben-Nun
Itay Hubara
Niv Giladi
Torsten Hoefler
Daniel Soudry
    ODL
ArXivPDFHTML

Papers citing "Augment your batch: better training with larger batches"

20 / 20 papers shown
Title
LookHere: Vision Transformers with Directed Attention Generalize and
  Extrapolate
LookHere: Vision Transformers with Directed Attention Generalize and Extrapolate
A. Fuller
Daniel G. Kyrollos
Yousef Yassin
James R. Green
43
2
0
22 May 2024
Poly-View Contrastive Learning
Poly-View Contrastive Learning
Amitis Shidani
Devon Hjelm
Jason Ramapuram
Russ Webb
Eeshan Gunesh Dhekane
Dan Busbridge
VLM
SSL
34
4
0
08 Mar 2024
A New Linear Scaling Rule for Private Adaptive Hyperparameter
  Optimization
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization
Ashwinee Panda
Xinyu Tang
Saeed Mahloujifar
Vikash Sehwag
Prateek Mittal
31
11
0
08 Dec 2022
Adaptive scaling of the learning rate by second order automatic
  differentiation
Adaptive scaling of the learning rate by second order automatic differentiation
F. Gournay
Alban Gossard
ODL
23
1
0
26 Oct 2022
R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS
R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS
Kyle Kastner
Aaron Courville
25
0
0
30 Jun 2022
CVNets: High Performance Library for Computer Vision
CVNets: High Performance Library for Computer Vision
Sachin Mehta
Farzad Abdolhosseini
Mohammad Rastegari
18
18
0
04 Jun 2022
Avoiding Overfitting: A Survey on Regularization Methods for
  Convolutional Neural Networks
Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks
C. F. G. Santos
João Paulo Papa
22
211
0
10 Jan 2022
AutoFormer: Searching Transformers for Visual Recognition
AutoFormer: Searching Transformers for Visual Recognition
Minghao Chen
Houwen Peng
Jianlong Fu
Haibin Ling
ViT
36
259
0
01 Jul 2021
Data augmentation in Bayesian neural networks and the cold posterior
  effect
Data augmentation in Bayesian neural networks and the cold posterior effect
Seth Nabarro
Stoil Ganev
Adrià Garriga-Alonso
Vincent Fortuin
Mark van der Wilk
Laurence Aitchison
BDL
21
37
0
10 Jun 2021
Hardware and Software Optimizations for Accelerating Deep Neural
  Networks: Survey of Current Trends, Challenges, and the Road Ahead
Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead
Maurizio Capra
Beatrice Bussolino
Alberto Marchisio
Guido Masera
Maurizio Martina
Muhammad Shafique
BDL
48
140
0
21 Dec 2020
Learning Loss for Test-Time Augmentation
Learning Loss for Test-Time Augmentation
Ildoo Kim
Younghoon Kim
Sungwoong Kim
OOD
18
90
0
22 Oct 2020
Review: Deep Learning in Electron Microscopy
Review: Deep Learning in Electron Microscopy
Jeffrey M. Ede
24
79
0
17 Sep 2020
UniformAugment: A Search-free Probabilistic Data Augmentation Approach
UniformAugment: A Search-free Probabilistic Data Augmentation Approach
Tom Ching LingChen
Ava Khonsari
Amirreza Lashkari
M. Nazari
Jaspreet Singh Sambee
M. Nascimento
14
58
0
31 Mar 2020
Topologically Densified Distributions
Topologically Densified Distributions
Christoph Hofer
Florian Graf
Marc Niethammer
Roland Kwitt
22
15
0
12 Feb 2020
Learning by Cheating
Learning by Cheating
Dian Chen
Brady Zhou
V. Koltun
Philipp Krahenbuhl
SSL
45
503
0
27 Dec 2019
Adversarial AutoAugment
Adversarial AutoAugment
Xinyu Zhang
Qiang-qiang Wang
Jian Andrew Zhang
Zhaobai Zhong
AAML
14
196
0
24 Dec 2019
Faster Neural Network Training with Data Echoing
Faster Neural Network Training with Data Echoing
Dami Choi
Alexandre Passos
Christopher J. Shallue
George E. Dahl
13
48
0
12 Jul 2019
MultiGrain: a unified image embedding for classes and instances
MultiGrain: a unified image embedding for classes and instances
Maxim Berman
Hervé Jégou
Andrea Vedaldi
Iasonas Kokkinos
Matthijs Douze
13
110
0
14 Feb 2019
RenderGAN: Generating Realistic Labeled Data
RenderGAN: Generating Realistic Labeled Data
Leon Sixt
Benjamin Wild
Tim Landgraf
GAN
158
175
0
04 Nov 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp
  Minima
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
N. Keskar
Dheevatsa Mudigere
J. Nocedal
M. Smelyanskiy
P. T. P. Tang
ODL
278
2,888
0
15 Sep 2016
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