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Towards Learning Convolutions from Scratch

Towards Learning Convolutions from Scratch

27 July 2020
Behnam Neyshabur
    SSL
ArXivPDFHTML

Papers citing "Towards Learning Convolutions from Scratch"

29 / 29 papers shown
Title
Meta-Learning Symmetries by Reparameterization
Meta-Learning Symmetries by Reparameterization
Allan Zhou
Tom Knowles
Chelsea Finn
OOD
79
95
0
06 Jul 2020
Rigging the Lottery: Making All Tickets Winners
Rigging the Lottery: Making All Tickets Winners
Utku Evci
Trevor Gale
Jacob Menick
Pablo Samuel Castro
Erich Elsen
187
602
0
25 Nov 2019
On the Relationship between Self-Attention and Convolutional Layers
On the Relationship between Self-Attention and Convolutional Layers
Jean-Baptiste Cordonnier
Andreas Loukas
Martin Jaggi
112
534
0
08 Nov 2019
Sparse Networks from Scratch: Faster Training without Losing Performance
Sparse Networks from Scratch: Faster Training without Losing Performance
Tim Dettmers
Luke Zettlemoyer
139
339
0
10 Jul 2019
Finding the Needle in the Haystack with Convolutions: on the benefits of
  architectural bias
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias
Stéphane dÁscoli
Levent Sagun
Joan Bruna
Giulio Biroli
62
37
0
16 Jun 2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan
Quoc V. Le
3DV
MedIm
139
18,115
0
28 May 2019
Fast AutoAugment
Fast AutoAugment
Sungbin Lim
Ildoo Kim
Taesup Kim
Chiheon Kim
Sungwoong Kim
97
595
0
01 May 2019
Bayesian Deep Convolutional Networks with Many Channels are Gaussian
  Processes
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes
Roman Novak
Lechao Xiao
Jaehoon Lee
Yasaman Bahri
Greg Yang
Jiri Hron
Daniel A. Abolafia
Jeffrey Pennington
Jascha Narain Sohl-Dickstein
UQCV
BDL
63
309
0
11 Oct 2018
SNIP: Single-shot Network Pruning based on Connection Sensitivity
SNIP: Single-shot Network Pruning based on Connection Sensitivity
Namhoon Lee
Thalaiyasingam Ajanthan
Philip Torr
VLM
257
1,199
0
04 Oct 2018
Implicit Bias of Gradient Descent on Linear Convolutional Networks
Implicit Bias of Gradient Descent on Linear Convolutional Networks
Suriya Gunasekar
Jason D. Lee
Daniel Soudry
Nathan Srebro
MDE
124
411
0
01 Jun 2018
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Jonathan Frankle
Michael Carbin
230
3,463
0
09 Mar 2018
Regularized Evolution for Image Classifier Architecture Search
Regularized Evolution for Image Classifier Architecture Search
Esteban Real
A. Aggarwal
Yanping Huang
Quoc V. Le
153
3,031
0
05 Feb 2018
Scalable Training of Artificial Neural Networks with Adaptive Sparse
  Connectivity inspired by Network Science
Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science
Decebal Constantin Mocanu
Elena Mocanu
Peter Stone
Phuong H. Nguyen
M. Gibescu
A. Liotta
172
631
0
15 Jul 2017
Exploring Generalization in Deep Learning
Exploring Generalization in Deep Learning
Behnam Neyshabur
Srinadh Bhojanapalli
David A. McAllester
Nathan Srebro
FAtt
148
1,255
0
27 Jun 2017
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds
Mahesh Chandra Mukkamala
Matthias Hein
ODL
54
258
0
17 Jun 2017
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural
  Networks with Many More Parameters than Training Data
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data
Gintare Karolina Dziugaite
Daniel M. Roy
106
813
0
31 Mar 2017
Understanding deep learning requires rethinking generalization
Understanding deep learning requires rethinking generalization
Chiyuan Zhang
Samy Bengio
Moritz Hardt
Benjamin Recht
Oriol Vinyals
HAI
339
4,626
0
10 Nov 2016
Neural Architecture Search with Reinforcement Learning
Neural Architecture Search with Reinforcement Learning
Barret Zoph
Quoc V. Le
459
5,372
0
05 Nov 2016
Convolution by Evolution: Differentiable Pattern Producing Networks
Convolution by Evolution: Differentiable Pattern Producing Networks
Chrisantha Fernando
Dylan Banarse
Malcolm Reynolds
F. Besse
David Pfau
Max Jaderberg
Marc Lanctot
Daan Wierstra
282
102
0
08 Jun 2016
Inductive Bias of Deep Convolutional Networks through Pooling Geometry
Inductive Bias of Deep Convolutional Networks through Pooling Geometry
Nadav Cohen
Amnon Shashua
58
134
0
22 May 2016
Identity Mappings in Deep Residual Networks
Identity Mappings in Deep Residual Networks
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
354
10,182
0
16 Mar 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
193,878
0
10 Dec 2015
How far can we go without convolution: Improving fully-connected
  networks
How far can we go without convolution: Improving fully-connected networks
Zhouhan Lin
Roland Memisevic
K. Konda
61
52
0
09 Nov 2015
Listen, Attend and Spell
Listen, Attend and Spell
William Chan
Navdeep Jaitly
Quoc V. Le
Oriol Vinyals
RALM
153
2,266
0
05 Aug 2015
Striving for Simplicity: The All Convolutional Net
Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg
Alexey Dosovitskiy
Thomas Brox
Martin Riedmiller
FAtt
248
4,667
0
21 Dec 2014
In Search of the Real Inductive Bias: On the Role of Implicit
  Regularization in Deep Learning
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
Behnam Neyshabur
Ryota Tomioka
Nathan Srebro
AI4CE
90
657
0
20 Dec 2014
Visualizing and Understanding Convolutional Networks
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler
Rob Fergus
FAtt
SSL
595
15,876
0
12 Nov 2013
Stochastic Pooling for Regularization of Deep Convolutional Neural
  Networks
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
Matthew D. Zeiler
Rob Fergus
190
989
0
16 Jan 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
453
7,661
0
03 Jul 2012
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