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Homogeneous Vector Capsules Enable Adaptive Gradient Descent in
  Convolutional Neural Networks
v1v2 (latest)

Homogeneous Vector Capsules Enable Adaptive Gradient Descent in Convolutional Neural Networks

20 June 2019
Adam Byerly
T. Kalganova
ArXiv (abs)PDFHTML

Papers citing "Homogeneous Vector Capsules Enable Adaptive Gradient Descent in Convolutional Neural Networks"

26 / 26 papers shown
Title
Pushing the Limits of Capsule Networks
Pushing the Limits of Capsule Networks
P. Nair
Rohan Doshi
Stefan Keselj
45
32
0
15 Mar 2021
Improving the Robustness of Capsule Networks to Image Affine
  Transformations
Improving the Robustness of Capsule Networks to Image Affine Transformations
Jindong Gu
Volker Tresp
3DPC
44
51
0
18 Nov 2019
Building Deep, Equivariant Capsule Networks
Building Deep, Equivariant Capsule Networks
Sairaam Venkatraman
S. Balasubramanian
R. R. Sarma
3DPC
38
27
0
04 Aug 2019
Capsule Networks Need an Improved Routing Algorithm
Capsule Networks Need an Improved Routing Algorithm
Inyoung Paik
Taeyeong Kwak
Injung Kim
115
35
0
31 Jul 2019
Path Capsule Networks
Path Capsule Networks
Mohammed Amer
Tomás Maul
3DPC
31
27
0
11 Feb 2019
Generalized Capsule Networks with Trainable Routing Procedure
Generalized Capsule Networks with Trainable Routing Procedure
Zhenhua Chen
David J. Crandall
3DPCMedIm
45
31
0
27 Aug 2018
Effects of Degradations on Deep Neural Network Architectures
Effects of Degradations on Deep Neural Network Architectures
Prasun Roy
Subhankar Ghosh
Saumik Bhattacharya
Umapada Pal
58
137
0
26 Jul 2018
Closing the Generalization Gap of Adaptive Gradient Methods in Training
  Deep Neural Networks
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Jinghui Chen
Dongruo Zhou
Yiqi Tang
Ziyan Yang
Yuan Cao
Quanquan Gu
ODL
79
193
0
18 Jun 2018
Averaging Weights Leads to Wider Optima and Better Generalization
Averaging Weights Leads to Wider Optima and Better Generalization
Pavel Izmailov
Dmitrii Podoprikhin
T. Garipov
Dmitry Vetrov
A. Wilson
FedMLMoMe
135
1,669
0
14 Mar 2018
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
T. Garipov
Pavel Izmailov
Dmitrii Podoprikhin
Dmitry Vetrov
A. Wilson
UQCV
83
755
0
27 Feb 2018
An Artificial Neural Network Architecture Based on Context
  Transformations in Cortical Minicolumns
An Artificial Neural Network Architecture Based on Context Transformations in Cortical Minicolumns
Vasily Morzhakov
A. Redozubov
AI4TS
32
2
0
16 Dec 2017
Dynamic Routing Between Capsules
Dynamic Routing Between Capsules
S. Sabour
Nicholas Frosst
Geoffrey E. Hinton
177
4,602
0
26 Oct 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning
  Algorithms
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao
Kashif Rasul
Roland Vollgraf
283
8,904
0
25 Aug 2017
The Marginal Value of Adaptive Gradient Methods in Machine Learning
The Marginal Value of Adaptive Gradient Methods in Machine Learning
Ashia Wilson
Rebecca Roelofs
Mitchell Stern
Nathan Srebro
Benjamin Recht
ODL
68
1,032
0
23 May 2017
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
  Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard
Menglong Zhu
Bo Chen
Dmitry Kalenichenko
Weijun Wang
Tobias Weyand
M. Andreetto
Hartwig Adam
3DH
1.2K
20,880
0
17 Apr 2017
Snapshot Ensembles: Train 1, get M for free
Snapshot Ensembles: Train 1, get M for free
Gao Huang
Yixuan Li
Geoff Pleiss
Zhuang Liu
John E. Hopcroft
Kilian Q. Weinberger
OODFedMLUQCV
134
951
0
01 Apr 2017
Xception: Deep Learning with Depthwise Separable Convolutions
Xception: Deep Learning with Depthwise Separable Convolutions
François Chollet
MDEBDLPINN
1.4K
14,596
0
07 Oct 2016
Inception-v4, Inception-ResNet and the Impact of Residual Connections on
  Learning
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Christian Szegedy
Sergey Ioffe
Vincent Vanhoucke
Alexander A. Alemi
381
14,260
0
23 Feb 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,322
0
10 Dec 2015
Rethinking the Inception Architecture for Computer Vision
Rethinking the Inception Architecture for Computer Vision
Christian Szegedy
Vincent Vanhoucke
Sergey Ioffe
Jonathon Shlens
Z. Wojna
3DVBDL
886
27,412
0
02 Dec 2015
Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
463
43,328
0
11 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.9K
150,260
0
22 Dec 2014
Going Deeper with Convolutions
Going Deeper with Convolutions
Christian Szegedy
Wei Liu
Yangqing Jia
P. Sermanet
Scott E. Reed
Dragomir Anguelov
D. Erhan
Vincent Vanhoucke
Andrew Rabinovich
480
43,685
0
17 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAttMDE
1.7K
100,479
0
04 Sep 2014
Network In Network
Network In Network
Min Lin
Qiang Chen
Shuicheng Yan
294
6,283
0
16 Dec 2013
Improving neural networks by preventing co-adaptation of feature
  detectors
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
455
7,666
0
03 Jul 2012
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