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Learning in the Machine: Random Backpropagation and the Deep Learning
  Channel

Learning in the Machine: Random Backpropagation and the Deep Learning Channel

8 December 2016
Pierre Baldi
Peter Sadowski
Zhiqin Lu
    AAML
ArXivPDFHTML

Papers citing "Learning in the Machine: Random Backpropagation and the Deep Learning Channel"

5 / 5 papers shown
Title
Feedback alignment in deep convolutional networks
Feedback alignment in deep convolutional networks
Theodore H. Moskovitz
Ashok Litwin-Kumar
L. F. Abbott
11
59
0
12 Dec 2018
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing
  Framework for Embedded Learning
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
Georgios Detorakis
Sadique Sheik
C. Augustine
Somnath Paul
Bruno U. Pedroni
N. Dutt
J. Krichmar
Gert Cauwenberghs
Emre Neftci
29
29
0
29 Sep 2017
SuperSpike: Supervised learning in multi-layer spiking neural networks
SuperSpike: Supervised learning in multi-layer spiking neural networks
Friedemann Zenke
Surya Ganguli
16
557
0
31 May 2017
Understanding Synthetic Gradients and Decoupled Neural Interfaces
Understanding Synthetic Gradients and Decoupled Neural Interfaces
Wojciech M. Czarnecki
G. Swirszcz
Max Jaderberg
Simon Osindero
Oriol Vinyals
Koray Kavukcuoglu
20
81
0
01 Mar 2017
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
260
7,634
0
03 Jul 2012
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