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Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
v1v2v3 (latest)

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

11 February 2015
Sergey Ioffe
Christian Szegedy
    OOD
ArXiv (abs)PDFHTML

Papers citing "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift"

50 / 11,282 papers shown
Title
70 years of machine learning in geoscience in review
70 years of machine learning in geoscience in review
Jesper Sören Dramsch
VLMAI4CE
113
163
0
16 Jun 2020
New Interpretations of Normalization Methods in Deep Learning
New Interpretations of Normalization Methods in Deep Learning
Jiacheng Sun
Xiangyong Cao
Hanwen Liang
Weiran Huang
Zewei Chen
Zhenguo Li
70
35
0
16 Jun 2020
Learning to Solve Vehicle Routing Problems with Time Windows through
  Joint Attention
Learning to Solve Vehicle Routing Problems with Time Windows through Joint Attention
Jonas K. Falkner
Lars Schmidt-Thieme
46
38
0
16 Jun 2020
Learning Rates as a Function of Batch Size: A Random Matrix Theory
  Approach to Neural Network Training
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training
Diego Granziol
S. Zohren
Stephen J. Roberts
ODL
148
50
0
16 Jun 2020
Multi-Objective CNN Based Algorithm for SAR Despeckling
Multi-Objective CNN Based Algorithm for SAR Despeckling
S. Vitale
G. Ferraioli
V. Pascazio
105
82
0
16 Jun 2020
NodeNet: A Graph Regularised Neural Network for Node Classification
NodeNet: A Graph Regularised Neural Network for Node Classification
Shrey Dabhi
Manojkumar Somabhai Parmar
GNN
53
11
0
16 Jun 2020
Measuring Model Complexity of Neural Networks with Curve Activation
  Functions
Measuring Model Complexity of Neural Networks with Curve Activation Functions
X. Hu
Weiqing Liu
Jiang Bian
J. Pei
63
22
0
16 Jun 2020
SPLASH: Learnable Activation Functions for Improving Accuracy and
  Adversarial Robustness
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness
Mohammadamin Tavakoli
Forest Agostinelli
Pierre Baldi
AAMLFAtt
191
39
0
16 Jun 2020
GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding
  Time-resolved EEG Motor Imagery Signals
GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals
Yimin Hou
Shuyue Jia
Xiangmin Lun
Hanrui Yang
Yan Shi
Yongqian Li
Shu Zhang
Jinglei Lv
61
118
0
16 Jun 2020
Robust Federated Learning: The Case of Affine Distribution Shifts
Robust Federated Learning: The Case of Affine Distribution Shifts
Amirhossein Reisizadeh
Farzan Farnia
Ramtin Pedarsani
Ali Jadbabaie
FedMLOOD
105
167
0
16 Jun 2020
Shape Matters: Understanding the Implicit Bias of the Noise Covariance
Shape Matters: Understanding the Implicit Bias of the Noise Covariance
Jeff Z. HaoChen
Colin Wei
Jason D. Lee
Tengyu Ma
222
95
0
15 Jun 2020
Multiscale Deep Equilibrium Models
Multiscale Deep Equilibrium Models
Shaojie Bai
V. Koltun
J. Zico Kolter
BDL
98
212
0
15 Jun 2020
On the training dynamics of deep networks with $L_2$ regularization
On the training dynamics of deep networks with L2L_2L2​ regularization
Aitor Lewkowycz
Guy Gur-Ari
118
54
0
15 Jun 2020
A Survey of Machine Learning Methods and Challenges for Windows Malware
  Classification
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification
Edward Raff
Charles K. Nicholas
AAML
76
57
0
15 Jun 2020
Learning Diverse and Discriminative Representations via the Principle of
  Maximal Coding Rate Reduction
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
Yaodong Yu
Kwan Ho Ryan Chan
Chong You
Chaobing Song
Yi-An Ma
SSL
99
198
0
15 Jun 2020
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Polina Kirichenko
Pavel Izmailov
A. Wilson
OODD
142
278
0
15 Jun 2020
Globally Injective ReLU Networks
Globally Injective ReLU Networks
Michael Puthawala
K. Kothari
Matti Lassas
Ivan Dokmanić
Maarten V. de Hoop
105
28
0
15 Jun 2020
Depth Uncertainty in Neural Networks
Depth Uncertainty in Neural Networks
Javier Antorán
J. Allingham
José Miguel Hernández-Lobato
UQCVOODBDL
125
103
0
15 Jun 2020
Spherical Motion Dynamics: Learning Dynamics of Neural Network with
  Normalization, Weight Decay, and SGD
Spherical Motion Dynamics: Learning Dynamics of Neural Network with Normalization, Weight Decay, and SGD
Ruosi Wan
Zhanxing Zhu
Xiangyu Zhang
Jian Sun
78
11
0
15 Jun 2020
Self-supervised Learning: Generative or Contrastive
Self-supervised Learning: Generative or Contrastive
Xiao Liu
Fanjin Zhang
Zhenyu Hou
Zhaoyu Wang
Li Mian
Jing Zhang
Jie Tang
SSL
226
1,650
0
15 Jun 2020
AdamP: Slowing Down the Slowdown for Momentum Optimizers on
  Scale-invariant Weights
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
Byeongho Heo
Sanghyuk Chun
Seong Joon Oh
Dongyoon Han
Sangdoo Yun
Gyuwan Kim
Youngjung Uh
Jung-Woo Ha
ODL
406
27
0
15 Jun 2020
Classifying degraded images over various levels of degradation
Classifying degraded images over various levels of degradation
Kazuki Endo
Masayuki Tanaka
Masatoshi Okutomi
36
15
0
15 Jun 2020
PCAAE: Principal Component Analysis Autoencoder for organising the
  latent space of generative networks
PCAAE: Principal Component Analysis Autoencoder for organising the latent space of generative networks
Chi-Hieu Pham
Saïd Ladjal
A. Newson
DRL
104
33
0
14 Jun 2020
Cascaded deep monocular 3D human pose estimation with evolutionary
  training data
Cascaded deep monocular 3D human pose estimation with evolutionary training data
Shichao Li
Lei Ke
Kevin Pratama
Yu-Wing Tai
Chi-Keung Tang
Kwang-Ting Cheng
3DH
97
158
0
14 Jun 2020
Exploiting the ConvLSTM: Human Action Recognition using Raw Depth
  Video-Based Recurrent Neural Networks
Exploiting the ConvLSTM: Human Action Recognition using Raw Depth Video-Based Recurrent Neural Networks
Adrián Sánchez-Caballero
D. Fuentes-Jiménez
Cristina Losada-Gutiérrez
76
24
0
13 Jun 2020
3DFCNN: Real-Time Action Recognition using 3D Deep Neural Networks with
  Raw Depth Information
3DFCNN: Real-Time Action Recognition using 3D Deep Neural Networks with Raw Depth Information
Adrián Sánchez-Caballero
Sergio de López-Diz
D. Fuentes-Jiménez
Cristina Losada-Gutiérrez
Marta Marrón-Romera
D. Casillas-Pérez
Mohammad Ibrahim Sarker
HAI
119
66
0
13 Jun 2020
DeeperGCN: All You Need to Train Deeper GCNs
DeeperGCN: All You Need to Train Deeper GCNs
Guohao Li
Chenxin Xiong
Ali K. Thabet
Guohao Li
GNN
293
443
0
13 Jun 2020
Bootstrap your own latent: A new approach to self-supervised Learning
Bootstrap your own latent: A new approach to self-supervised Learning
Jean-Bastien Grill
Florian Strub
Florent Altché
Corentin Tallec
Pierre Harvey Richemond
...
M. G. Azar
Bilal Piot
Koray Kavukcuoglu
Rémi Munos
Michal Valko
SSL
527
6,881
0
13 Jun 2020
Beyond Random Matrix Theory for Deep Networks
Beyond Random Matrix Theory for Deep Networks
Diego Granziol
127
16
0
13 Jun 2020
Adversarial Self-Supervised Contrastive Learning
Adversarial Self-Supervised Contrastive Learning
Minseon Kim
Jihoon Tack
Sung Ju Hwang
SSL
102
251
0
13 Jun 2020
GAN Memory with No Forgetting
GAN Memory with No Forgetting
Yulai Cong
Miaoyun Zhao
Jianqiao Li
Sijia Wang
Lawrence Carin
CLL
89
124
0
13 Jun 2020
AlgebraNets
AlgebraNets
Jordan Hoffmann
Simon Schmitt
Simon Osindero
Karen Simonyan
Erich Elsen
MoE
193
6
0
12 Jun 2020
Ensemble Distillation for Robust Model Fusion in Federated Learning
Ensemble Distillation for Robust Model Fusion in Federated Learning
Tao R. Lin
Lingjing Kong
Sebastian U. Stich
Martin Jaggi
FedML
153
1,063
0
12 Jun 2020
Branch-Cooperative OSNet for Person Re-Identification
Branch-Cooperative OSNet for Person Re-Identification
Lei Zhang
Xiaofu Wu
Suofei Zhang
Zirui Yin
3DPC
28
4
0
12 Jun 2020
Are we done with ImageNet?
Are we done with ImageNet?
Lucas Beyer
Olivier J. Hénaff
Alexander Kolesnikov
Xiaohua Zhai
Aaron van den Oord
VLM
168
408
0
12 Jun 2020
Kernelized information bottleneck leads to biologically plausible
  3-factor Hebbian learning in deep networks
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks
Roman Pogodin
P. Latham
149
35
0
12 Jun 2020
Understanding and Resolving Performance Degradation in Graph
  Convolutional Networks
Understanding and Resolving Performance Degradation in Graph Convolutional Networks
Kuangqi Zhou
Yanfei Dong
Kaixin Wang
W. Lee
Bryan Hooi
Huan Xu
Jiashi Feng
GNNBDL
133
94
0
12 Jun 2020
Learning the Travelling Salesperson Problem Requires Rethinking
  Generalization
Learning the Travelling Salesperson Problem Requires Rethinking Generalization
Chaitanya K. Joshi
Quentin Cappart
Louis-Martin Rousseau
T. Laurent
267
122
0
12 Jun 2020
A benchmark study on reliable molecular supervised learning via Bayesian
  learning
A benchmark study on reliable molecular supervised learning via Bayesian learning
Doyeong Hwang
Grace Lee
Hanseok Jo
Seyoul Yoon
Seongok Ryu
84
9
0
12 Jun 2020
Non-Negative Bregman Divergence Minimization for Deep Direct Density
  Ratio Estimation
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation
Masahiro Kato
Takeshi Teshima
96
36
0
12 Jun 2020
Towards Robust Pattern Recognition: A Review
Towards Robust Pattern Recognition: A Review
Xu-Yao Zhang
Cheng-Lin Liu
C. Suen
OODHAI
73
110
0
12 Jun 2020
Towards Deeper Graph Neural Networks with Differentiable Group
  Normalization
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Kaixiong Zhou
Xiao Huang
Yuening Li
Daochen Zha
Rui Chen
Helen Zhou
144
205
0
12 Jun 2020
Understanding the Role of Training Regimes in Continual Learning
Understanding the Role of Training Regimes in Continual Learning
Seyed Iman Mirzadeh
Mehrdad Farajtabar
Razvan Pascanu
H. Ghasemzadeh
CLL
81
228
0
12 Jun 2020
Reintroducing Straight-Through Estimators as Principled Methods for
  Stochastic Binary Networks
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks
Alexander Shekhovtsov
Dmitry Molchanov
MQ
85
16
0
11 Jun 2020
Ansor: Generating High-Performance Tensor Programs for Deep Learning
Ansor: Generating High-Performance Tensor Programs for Deep Learning
Lianmin Zheng
Chengfan Jia
Minmin Sun
Zhao Wu
Cody Hao Yu
...
Jun Yang
Danyang Zhuo
Koushik Sen
Joseph E. Gonzalez
Ion Stoica
157
403
0
11 Jun 2020
End-to-end Sinkhorn Autoencoder with Noise Generator
End-to-end Sinkhorn Autoencoder with Noise Generator
Kamil Deja
Jan Dubiñski
Piotr W. Nowak
S. Wenzel
Tomasz Trzciñski
SyDa
60
23
0
11 Jun 2020
VirTex: Learning Visual Representations from Textual Annotations
VirTex: Learning Visual Representations from Textual Annotations
Karan Desai
Justin Johnson
SSLVLM
184
437
0
11 Jun 2020
Unsupervised Learning of 3D Point Set Registration
Unsupervised Learning of 3D Point Set Registration
Lingjing Wang
Xiang Li
Yi Fang
3DPC
49
10
0
11 Jun 2020
An Edge Information and Mask Shrinking Based Image Inpainting Approach
An Edge Information and Mask Shrinking Based Image Inpainting Approach
Huali Xu
Xiangdong Su
Meng Wang
Xiang Hao
Guanglai Gao
36
2
0
11 Jun 2020
Dataset Condensation with Gradient Matching
Dataset Condensation with Gradient Matching
Bo Zhao
Konda Reddy Mopuri
Hakan Bilen
DD
185
505
0
10 Jun 2020
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