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Gradient-based Hyperparameter Optimization through Reversible Learning

Gradient-based Hyperparameter Optimization through Reversible Learning

11 February 2015
D. Maclaurin
David Duvenaud
Ryan P. Adams
    DD
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Papers citing "Gradient-based Hyperparameter Optimization through Reversible Learning"

47 / 497 papers shown
Title
SLAQ: Quality-Driven Scheduling for Distributed Machine Learning
SLAQ: Quality-Driven Scheduling for Distributed Machine Learning
Haoyu Zhang
Logan Stafman
Andrew Or
M. Freedman
35
140
0
13 Feb 2018
Predict and Constrain: Modeling Cardinality in Deep Structured
  Prediction
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Nataly Brukhim
Amir Globerson
29
9
0
13 Feb 2018
Semi-Amortized Variational Autoencoders
Semi-Amortized Variational Autoencoders
Yoon Kim
Sam Wiseman
Andrew C. Miller
David Sontag
Alexander M. Rush
BDL
DRL
33
243
0
07 Feb 2018
Meta-Tracker: Fast and Robust Online Adaptation for Visual Object
  Trackers
Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers
Eunbyung Park
Alexander C. Berg
VOT
TTA
38
166
0
09 Jan 2018
A Bridge Between Hyperparameter Optimization and Learning-to-learn
A Bridge Between Hyperparameter Optimization and Learning-to-learn
Luca Franceschi
Michele Donini
P. Frasconi
Massimiliano Pontil
35
20
0
18 Dec 2017
Nonparametric Neural Networks
Nonparametric Neural Networks
George Philipp
J. Carbonell
14
21
0
14 Dec 2017
Extreme Dimension Reduction for Handling Covariate Shift
Extreme Dimension Reduction for Handling Covariate Shift
Fulton Wang
Cynthia Rudin
25
1
0
29 Nov 2017
Towards Poisoning of Deep Learning Algorithms with Back-gradient
  Optimization
Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization
Luis Muñoz-González
Battista Biggio
Ambra Demontis
Andrea Paudice
Vasin Wongrassamee
Emil C. Lupu
Fabio Roli
AAML
15
624
0
29 Aug 2017
Image Augmentation using Radial Transform for Training Deep Neural
  Networks
Image Augmentation using Radial Transform for Training Deep Neural Networks
Hojjat Salehinejad
S. Valaee
T. Dowdell
Joseph Barfett
16
11
0
14 Aug 2017
Analysis and Optimization of Convolutional Neural Network Architectures
Analysis and Optimization of Convolutional Neural Network Architectures
Martin Thoma
22
72
0
31 Jul 2017
Discretization-free Knowledge Gradient Methods for Bayesian Optimization
Jian Wu
P. Frazier
BDL
13
9
0
20 Jul 2017
The Reversible Residual Network: Backpropagation Without Storing
  Activations
The Reversible Residual Network: Backpropagation Without Storing Activations
Aidan Gomez
Mengye Ren
R. Urtasun
Roger C. Grosse
25
542
0
14 Jul 2017
Kafnets: kernel-based non-parametric activation functions for neural
  networks
Kafnets: kernel-based non-parametric activation functions for neural networks
Simone Scardapane
S. Van Vaerenbergh
Simone Totaro
A. Uncini
19
12
0
13 Jul 2017
SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter
  Optimization
SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization
Jeffery Kinnison
Nathaniel Kremer-Herman
D. Thain
Walter J. Scheirer
17
11
0
05 Jul 2017
A Closer Look at Memorization in Deep Networks
A Closer Look at Memorization in Deep Networks
Devansh Arpit
Stanislaw Jastrzebski
Nicolas Ballas
David M. Krueger
Emmanuel Bengio
...
Tegan Maharaj
Asja Fischer
Aaron Courville
Yoshua Bengio
Simon Lacoste-Julien
TDI
17
1,785
0
16 Jun 2017
Stochastic Training of Neural Networks via Successive Convex
  Approximations
Stochastic Training of Neural Networks via Successive Convex Approximations
Simone Scardapane
P. Di Lorenzo
22
9
0
15 Jun 2017
Hyperparameter Optimization: A Spectral Approach
Hyperparameter Optimization: A Spectral Approach
Elad Hazan
Adam R. Klivans
Yang Yuan
27
118
0
02 Jun 2017
Reinforcement Learning for Learning Rate Control
Reinforcement Learning for Learning Rate Control
Chang Xu
Tao Qin
G. Wang
Tie-Yan Liu
16
34
0
31 May 2017
End-to-end representation learning for Correlation Filter based tracking
End-to-end representation learning for Correlation Filter based tracking
Jack Valmadre
Luca Bertinetto
João F. Henriques
Andrea Vedaldi
Philip Torr
36
1,396
0
20 Apr 2017
Exploiting gradients and Hessians in Bayesian optimization and Bayesian
  quadrature
Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature
Anqi Wu
Mikio C. Aoi
Jonathan W. Pillow
17
41
0
31 Mar 2017
Gradient-based Regularization Parameter Selection for Problems with
  Non-smooth Penalty Functions
Gradient-based Regularization Parameter Selection for Problems with Non-smooth Penalty Functions
Jean Feng
N. Simon
14
20
0
28 Mar 2017
End-to-End Learning for Structured Prediction Energy Networks
End-to-End Learning for Structured Prediction Energy Networks
David Belanger
Bishan Yang
Andrew McCallum
14
136
0
16 Mar 2017
Online Learning Rate Adaptation with Hypergradient Descent
Online Learning Rate Adaptation with Hypergradient Descent
A. G. Baydin
R. Cornish
David Martínez-Rubio
Mark W. Schmidt
Frank Wood
ODL
30
242
0
14 Mar 2017
Bayesian Optimization with Gradients
Bayesian Optimization with Gradients
Jian Wu
Matthias Poloczek
A. Wilson
P. Frazier
21
209
0
13 Mar 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
371
11,700
0
09 Mar 2017
Forward and Reverse Gradient-Based Hyperparameter Optimization
Forward and Reverse Gradient-Based Hyperparameter Optimization
Luca Franceschi
Michele Donini
P. Frasconi
Massimiliano Pontil
133
409
0
06 Mar 2017
Meta Networks
Meta Networks
Tsendsuren Munkhdalai
Hong-ye Yu
GNN
AI4CE
21
1,058
0
02 Mar 2017
Learning to Optimize Neural Nets
Learning to Optimize Neural Nets
Ke Li
Jitendra Malik
21
130
0
01 Mar 2017
Variational Inference using Implicit Distributions
Variational Inference using Implicit Distributions
Ferenc Huszár
DRL
GAN
16
135
0
27 Feb 2017
Structured Attention Networks
Structured Attention Networks
Yoon Kim
Carl Denton
Luong Hoang
Alexander M. Rush
41
460
0
03 Feb 2017
An Introduction to Deep Learning for the Physical Layer
An Introduction to Deep Learning for the Physical Layer
Tim O'Shea
J. Hoydis
AI4CE
89
2,172
0
02 Feb 2017
Tricks from Deep Learning
Tricks from Deep Learning
A. G. Baydin
Barak A. Pearlmutter
J. Siskind
ODL
16
9
0
10 Nov 2016
Unrolled Generative Adversarial Networks
Unrolled Generative Adversarial Networks
Luke Metz
Ben Poole
David Pfau
Jascha Narain Sohl-Dickstein
GAN
59
1,001
0
07 Nov 2016
Neural Network Architecture Optimization through Submodularity and Supermodularity
Junqi Jin
Ziang Yan
Kun Fu
Nan Jiang
Changshui Zhang
22
11
0
01 Sep 2016
Optimizing Recurrent Neural Networks Architectures under Time Constraints
Junqi Jin
Ziang Yan
Kun Fu
Nan Jiang
Changshui Zhang
22
2
0
29 Aug 2016
Learning to Optimize
Learning to Optimize
Ke Li
Jitendra Malik
13
252
0
06 Jun 2016
Deep Q-Networks for Accelerating the Training of Deep Neural Networks
Jie Fu
AI4CE
32
11
0
05 Jun 2016
Asymptotically exact inference in differentiable generative models
Asymptotically exact inference in differentiable generative models
Matthew M. Graham
Amos J. Storkey
BDL
21
33
0
25 May 2016
Programming with a Differentiable Forth Interpreter
Programming with a Differentiable Forth Interpreter
Matko Bosnjak
Tim Rocktaschel
Jason Naradowsky
Sebastian Riedel
17
148
0
21 May 2016
Hyperparameter optimization with approximate gradient
Hyperparameter optimization with approximate gradient
Fabian Pedregosa
37
442
0
07 Feb 2016
DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing
  Hyperparameters of Deep Neural Networks
DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks
Jie Fu
Hongyin Luo
Jiashi Feng
K. H. Low
Tat-Seng Chua
24
27
0
05 Jan 2016
Scalable Gradient-Based Tuning of Continuous Regularization
  Hyperparameters
Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
Jelena Luketina
Mathias Berglund
Klaus Greff
T. Raiko
27
173
0
20 Nov 2015
Structured Prediction Energy Networks
Structured Prediction Energy Networks
David Belanger
Andrew McCallum
GNN
18
219
0
19 Nov 2015
Diversity Networks: Neural Network Compression Using Determinantal Point
  Processes
Diversity Networks: Neural Network Compression Using Determinantal Point Processes
Zelda E. Mariet
S. Sra
11
129
0
16 Nov 2015
Speed learning on the fly
Speed learning on the fly
Pierre-Yves Massé
Yann Ollivier
23
13
0
08 Nov 2015
Early Stopping is Nonparametric Variational Inference
Early Stopping is Nonparametric Variational Inference
D. Maclaurin
David Duvenaud
Ryan P. Adams
BDL
30
95
0
06 Apr 2015
Automatic differentiation in machine learning: a survey
Automatic differentiation in machine learning: a survey
A. G. Baydin
Barak A. Pearlmutter
Alexey Radul
J. Siskind
PINN
AI4CE
ODL
54
2,750
0
20 Feb 2015
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