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Neural Sampling Machine with Stochastic Synapse allows Brain-like
  Learning and Inference

Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference

20 February 2021
S. Dutta
Georgios Detorakis
A. Khanna
B. Grisafe
Emre Neftci
S. Datta
    BDL
ArXivPDFHTML

Papers citing "Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference"

9 / 9 papers shown
Title
Lightweight, error-tolerant edge detection using memristor-enabled
  stochastic logics
Lightweight, error-tolerant edge detection using memristor-enabled stochastic logics
Lekai Song
Pengyu Liu
Jingfang Pei
Yang Liu
Songwei Liu
...
Leonard W. T. Ng
Tawfique Hasan
Kong-Pang Pun
Shuo Gao
Guohua Hu
49
0
0
25 Feb 2024
NeuSpin: Design of a Reliable Edge Neuromorphic System Based on
  Spintronics for Green AI
NeuSpin: Design of a Reliable Edge Neuromorphic System Based on Spintronics for Green AI
Soyed Tuhin Ahmed
K. Danouchi
G. Prenat
L. Anghel
M. Tahoori
12
0
0
11 Jan 2024
Synaptic Sampling of Neural Networks
Synaptic Sampling of Neural Networks
J. Aimone
William M. Severa
J. D. Smith
BDL
12
1
0
21 Nov 2023
Deep Neuromorphic Networks with Superconducting Single Flux Quanta
Deep Neuromorphic Networks with Superconducting Single Flux Quanta
G. Krylov
Alexander J. Edwards
Joseph S. Friedman
Eby G. Friedman
20
4
0
21 Sep 2023
Toward stochastic neural computing
Toward stochastic neural computing
Yang Qi
Zhichao Zhu
Yiming Wei
Lu Cao
Zhigang Wang
Jie Zhang
Wenlian Lu
Jianfeng Feng
14
3
0
23 May 2023
Binary stochasticity enabled highly efficient neuromorphic deep learning
  achieves better-than-software accuracy
Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy
Yang Li
Wei Wang
Ming Wang
C. Dou
Zhengyu Ma
...
Guanhua Yang
Feng Zhang
Ling Li
Daniele Ielmini
Ming-Yu Liu
16
5
0
25 Apr 2023
Sequence learning in a spiking neuronal network with memristive synapses
Sequence learning in a spiking neuronal network with memristive synapses
Younes Bouhadjar
S. Siegel
Tom Tetzlaff
M. Diesmann
Rainer Waser
D. Wouters
11
3
0
29 Nov 2022
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,136
0
06 Jun 2015
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
266
7,634
0
03 Jul 2012
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