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Eigendecomposition-Free Training of Deep Networks for Linear
  Least-Square Problems

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

15 April 2020
Zheng Dang
K. M. Yi
Yinlin Hu
Fei Wang
Pascal Fua
Mathieu Salzmann
ArXivPDFHTML

Papers citing "Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems"

13 / 13 papers shown
Title
Eigendecomposition-free Training of Deep Networks with Zero
  Eigenvalue-based Losses
Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses
Zheng Dang
K. M. Yi
Yinlin Hu
Fei Wang
Pascal Fua
Mathieu Salzmann
40
48
0
21 Mar 2018
Learning to Find Good Correspondences
Learning to Find Good Correspondences
K. M. Yi
Eduard Trulls
Y. Ono
Vincent Lepetit
Mathieu Salzmann
Pascal Fua
3DV
51
477
0
16 Nov 2017
Generic 3D Representation via Pose Estimation and Matching
Generic 3D Representation via Pose Estimation and Matching
Amir Zamir
T. Wekel
Pulkit Agrawal
Colin Wei
Jitendra Malik
Silvio Savarese
3DV
33
94
0
23 Oct 2017
Deep Learning on Lie Groups for Skeleton-based Action Recognition
Deep Learning on Lie Groups for Skeleton-based Action Recognition
Zhiwu Huang
Chengde Wan
Thomas Probst
Luc Van Gool
3DH
49
278
0
18 Dec 2016
DeMoN: Depth and Motion Network for Learning Monocular Stereo
DeMoN: Depth and Motion Network for Learning Monocular Stereo
Benjamin Ummenhofer
Huizhong Zhou
J. Uhrig
N. Mayer
Eddy Ilg
Alexey Dosovitskiy
Thomas Brox
3DV
MDE
82
701
0
07 Dec 2016
DSAC - Differentiable RANSAC for Camera Localization
DSAC - Differentiable RANSAC for Camera Localization
Eric Brachmann
Alexander Krull
Sebastian Nowozin
Jamie Shotton
Frank Michel
Stefan Gumhold
Carsten Rother
58
596
0
17 Nov 2016
A Riemannian Network for SPD Matrix Learning
A Riemannian Network for SPD Matrix Learning
Zhiwu Huang
Luc Van Gool
45
393
0
15 Aug 2016
gvnn: Neural Network Library for Geometric Computer Vision
gvnn: Neural Network Library for Geometric Computer Vision
Ankur Handa
Michael Blösch
Viorica Patraucean
Simon Stent
J. McCormac
Andrew J. Davison
ViT
41
98
0
25 Jul 2016
On Differentiating Parameterized Argmin and Argmax Problems with
  Application to Bi-level Optimization
On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization
Stephen Gould
Basura Fernando
A. Cherian
Peter Anderson
Rodrigo Santa Cruz
Edison Guo
36
223
0
19 Jul 2016
TensorFlow: A system for large-scale machine learning
TensorFlow: A system for large-scale machine learning
Martín Abadi
P. Barham
Jianmin Chen
Zhiwen Chen
Andy Davis
...
Vijay Vasudevan
Pete Warden
Martin Wicke
Yuan Yu
Xiaoqiang Zhang
GNN
AI4CE
338
18,300
0
27 May 2016
Differentiation of the Cholesky decomposition
Differentiation of the Cholesky decomposition
Iain Murray
45
37
0
24 Feb 2016
Spatial Transformer Networks
Spatial Transformer Networks
Max Jaderberg
Karen Simonyan
Andrew Zisserman
Koray Kavukcuoglu
264
7,361
0
05 Jun 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
852
149,474
0
22 Dec 2014
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