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On the effects of biased quantum random numbers on the initialization of
  artificial neural networks

On the effects of biased quantum random numbers on the initialization of artificial neural networks

30 August 2021
R. Heese
Moritz Wolter
Sascha Mucke
L. Franken
Nico Piatkowski
ArXivPDFHTML

Papers citing "On the effects of biased quantum random numbers on the initialization of artificial neural networks"

4 / 4 papers shown
Title
Torch.manual_seed(3407) is all you need: On the influence of random
  seeds in deep learning architectures for computer vision
Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision
David Picard
3DV
VLM
48
90
0
16 Sep 2021
Pseudo Random Number Generation: a Reinforcement Learning approach
Pseudo Random Number Generation: a Reinforcement Learning approach
Luca Pasqualini
Maurizio Parton
36
26
0
15 Dec 2019
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
  ImageNet Classification
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
VLM
221
18,534
0
06 Feb 2015
ADADELTA: An Adaptive Learning Rate Method
ADADELTA: An Adaptive Learning Rate Method
Matthew D. Zeiler
ODL
117
6,619
0
22 Dec 2012
1