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Bayesian Neural Networks at Scale: A Performance Analysis and Pruning
  Study

Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study

23 May 2020
Himanshu Sharma
Elise Jennings
    BDL
ArXivPDFHTML

Papers citing "Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study"

25 / 25 papers shown
Title
Decision-Making with Auto-Encoding Variational Bayes
Decision-Making with Auto-Encoding Variational Bayes
Romain Lopez
Pierre Boyeau
Nir Yosef
Michael I. Jordan
Jeffrey Regier
BDL
107
10,591
0
17 Feb 2020
Exascale Deep Learning for Scientific Inverse Problems
Exascale Deep Learning for Scientific Inverse Problems
N. Laanait
Josh Romero
Junqi Yin
M. T. Young
Sean Treichler
V. Starchenko
A. Borisevich
Alexander Sergeev
Michael A. Matheson
FedML
BDL
35
29
0
24 Sep 2019
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at
  Scale
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
A. G. Baydin
Lei Shao
W. Bhimji
Lukas Heinrich
Lawrence Meadows
...
Philip Torr
Victor W. Lee
Kyle Cranmer
P. Prabhat
Frank Wood
38
55
0
08 Jul 2019
Cyclical Annealing Schedule: A Simple Approach to Mitigating KL
  Vanishing
Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing
Hao Fu
Chunyuan Li
Xiaodong Liu
Jianfeng Gao
Asli Celikyilmaz
Lawrence Carin
ODL
44
362
0
25 Mar 2019
A Comprehensive guide to Bayesian Convolutional Neural Network with
  Variational Inference
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
Kumar Shridhar
F. Laumann
Marcus Liwicki
BDL
UQCV
60
172
0
08 Jan 2019
Bayesian Layers: A Module for Neural Network Uncertainty
Bayesian Layers: A Module for Neural Network Uncertainty
Dustin Tran
Michael W. Dusenberry
Mark van der Wilk
Danijar Hafner
UQCV
BDL
82
121
0
10 Dec 2018
Mesh-TensorFlow: Deep Learning for Supercomputers
Mesh-TensorFlow: Deep Learning for Supercomputers
Noam M. Shazeer
Youlong Cheng
Niki Parmar
Dustin Tran
Ashish Vaswani
...
HyoukJoong Lee
O. Milenkovic
C. Young
Ryan Sepassi
Blake Hechtman
GNN
MoE
AI4CE
38
387
0
05 Nov 2018
Pyro: Deep Universal Probabilistic Programming
Pyro: Deep Universal Probabilistic Programming
Eli Bingham
Jonathan P. Chen
M. Jankowiak
F. Obermeyer
Neeraj Pradhan
Theofanis Karaletsos
Rohit Singh
Paul A. Szerlip
Paul Horsfall
Noah D. Goodman
BDL
GP
81
1,043
0
18 Oct 2018
Flipout: Efficient Pseudo-Independent Weight Perturbations on
  Mini-Batches
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Yeming Wen
Paul Vicol
Jimmy Ba
Dustin Tran
Roger C. Grosse
BDL
33
308
0
12 Mar 2018
Horovod: fast and easy distributed deep learning in TensorFlow
Horovod: fast and easy distributed deep learning in TensorFlow
Alexander Sergeev
Mike Del Balso
47
1,217
0
15 Feb 2018
TensorFlow Distributions
TensorFlow Distributions
Joshua V. Dillon
I. Langmore
Dustin Tran
E. Brevdo
Srinivas Vasudevan
David A. Moore
Brian Patton
Alexander A. Alemi
Matt Hoffman
Rif A. Saurous
GP
72
349
0
28 Nov 2017
Fixing a Broken ELBO
Fixing a Broken ELBO
Alexander A. Alemi
Ben Poole
Ian S. Fischer
Joshua V. Dillon
Rif A. Saurous
Kevin Patrick Murphy
DRL
BDL
50
80
0
01 Nov 2017
CHAOS: A Parallelization Scheme for Training Convolutional Neural
  Networks on Intel Xeon Phi
CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi
Andre Viebke
Suejb Memeti
Sabri Pllana
Ajith Abraham
54
25
0
25 Feb 2017
Reparameterization Gradients through Acceptance-Rejection Sampling
  Algorithms
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
C. A. Naesseth
Francisco J. R. Ruiz
Scott W. Linderman
David M. Blei
BDL
96
109
0
18 Oct 2016
Variational Inference: A Review for Statisticians
Variational Inference: A Review for Statisticians
David M. Blei
A. Kucukelbir
Jon D. McAuliffe
BDL
133
4,748
0
04 Jan 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
868
192,638
0
10 Dec 2015
Generating Sentences from a Continuous Space
Generating Sentences from a Continuous Space
Samuel R. Bowman
Luke Vilnis
Oriol Vinyals
Andrew M. Dai
Rafal Jozefowicz
Samy Bengio
DRL
56
2,352
0
19 Nov 2015
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
388
9,233
0
06 Jun 2015
Weight Uncertainty in Neural Networks
Weight Uncertainty in Neural Networks
Charles Blundell
Julien Cornebise
Koray Kavukcuoglu
Daan Wierstra
UQCV
BDL
66
1,878
0
20 May 2015
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAtt
MDE
537
99,991
0
04 Sep 2014
Black Box Variational Inference
Black Box Variational Inference
Rajesh Ranganath
S. Gerrish
David M. Blei
DRL
BDL
50
1,157
0
31 Dec 2013
Auto-Encoding Variational Bayes
Auto-Encoding Variational Bayes
Diederik P. Kingma
Max Welling
BDL
295
16,972
0
20 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
324
7,650
0
03 Jul 2012
Stochastic Variational Inference
Stochastic Variational Inference
Matt Hoffman
David M. Blei
Chong-Jun Wang
John Paisley
BDL
133
2,605
0
29 Jun 2012
Variational Bayesian Inference with Stochastic Search
Variational Bayesian Inference with Stochastic Search
John Paisley
David M. Blei
Michael I. Jordan
BDL
57
496
0
27 Jun 2012
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