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Learning to superoptimize programs - Workshop Version

Learning to superoptimize programs - Workshop Version

4 December 2016
Rudy Bunel
Alban Desmaison
M. P. Kumar
Philip Torr
Pushmeet Kohli
ArXiv (abs)PDFHTML

Papers citing "Learning to superoptimize programs - Workshop Version"

9 / 9 papers shown
Title
Learning to learn by gradient descent by gradient descent
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz
Misha Denil
Sergio Gomez Colmenarejo
Matthew W. Hoffman
David Pfau
Tom Schaul
Brendan Shillingford
Nando de Freitas
124
2,008
0
14 Jun 2016
Learning to Optimize
Learning to Optimize
Ke Li
Jitendra Malik
63
257
0
06 Jun 2016
Adaptive Neural Compilation
Adaptive Neural Compilation
Rudy Bunel
Alban Desmaison
Pushmeet Kohli
Philip Torr
P. Mudigonda
79
46
0
25 May 2016
Inference Networks for Sequential Monte Carlo in Graphical Models
Inference Networks for Sequential Monte Carlo in Graphical Models
Brooks Paige
Frank Wood
BDL
165
110
0
22 Feb 2016
Gradient Estimation Using Stochastic Computation Graphs
Gradient Estimation Using Stochastic Computation Graphs
John Schulman
N. Heess
T. Weber
Pieter Abbeel
OffRL
152
395
0
17 Jun 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
2.1K
150,364
0
22 Dec 2014
Neural Turing Machines
Neural Turing Machines
Alex Graves
Greg Wayne
Ivo Danihelka
111
2,333
0
20 Oct 2014
Learning to Discover Efficient Mathematical Identities
Learning to Discover Efficient Mathematical Identities
Wojciech Zaremba
Karol Kurach
Rob Fergus
88
54
0
06 Jun 2014
The Informed Sampler: A Discriminative Approach to Bayesian Inference in
  Generative Computer Vision Models
The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models
Varun Jampani
Sebastian Nowozin
M. Loper
Peter V. Gehler
95
45
0
04 Feb 2014
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