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Learning in the Machine: To Share or Not to Share?
v1v2 (latest)

Learning in the Machine: To Share or Not to Share?

23 September 2019
J. Ott
Erik J. Linstead
Nicholas LaHaye
Pierre Baldi
ArXiv (abs)PDFHTML

Papers citing "Learning in the Machine: To Share or Not to Share?"

13 / 13 papers shown
Title
Questions to Guide the Future of Artificial Intelligence Research
Questions to Guide the Future of Artificial Intelligence Research
J. Ott
24
3
0
21 Dec 2019
Assessing the Scalability of Biologically-Motivated Deep Learning
  Algorithms and Architectures
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
Sergey Bartunov
Adam Santoro
Blake A. Richards
Luke Marris
Geoffrey E. Hinton
Timothy Lillicrap
89
244
0
12 Jul 2018
Learning in the Machine: the Symmetries of the Deep Learning Channel
Learning in the Machine: the Symmetries of the Deep Learning Channel
Pierre Baldi
Peter Sadowski
Zhiqin Lu
54
30
0
22 Dec 2017
Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning
Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning
Peter Sadowski
B. Radics
Ananya
Y. Yamazaki
Pierre Baldi
25
12
0
06 Jun 2017
A Convolutional Neural Network Neutrino Event Classifier
A Convolutional Neural Network Neutrino Event Classifier
A. Aurisano
A. Radovic
D. Rocco
A. Himmel
M. Messier
E. Niner
G. Pawloski
F. Psihas
A. Sousa
P. Vahle
BDL
34
207
0
05 Apr 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
Training Very Deep Networks
Training Very Deep Networks
R. Srivastava
Klaus Greff
Jürgen Schmidhuber
161
1,685
0
22 Jul 2015
A Theory of Local Learning, the Learning Channel, and the Optimality of
  Backpropagation
A Theory of Local Learning, the Learning Channel, and the Optimality of Backpropagation
Pierre Baldi
Peter Sadowski
51
72
0
22 Jun 2015
Difference Target Propagation
Difference Target Propagation
Dong-Hyun Lee
Saizheng Zhang
Asja Fischer
Yoshua Bengio
AAML
96
352
0
23 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
Deep Neural Networks Rival the Representation of Primate IT Cortex for
  Core Visual Object Recognition
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
C. Cadieu
Ha Hong
Daniel L. K. Yamins
Nicolas Pinto
Diego Ardila
E. Solomon
N. Majaj
J. DiCarlo
94
787
0
12 Jun 2014
Learning Phrase Representations using RNN Encoder-Decoder for
  Statistical Machine Translation
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Kyunghyun Cho
B. V. Merrienboer
Çağlar Gülçehre
Dzmitry Bahdanau
Fethi Bougares
Holger Schwenk
Yoshua Bengio
AIMat
1.1K
23,370
0
03 Jun 2014
Deep Learning in Neural Networks: An Overview
Deep Learning in Neural Networks: An Overview
Jürgen Schmidhuber
HAI
246
16,373
0
30 Apr 2014
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