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Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic
  Differentiation

Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

10 March 2016
James Townsend
Niklas Koep
S. Weichwald
ArXivPDFHTML

Papers citing "Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation"

42 / 42 papers shown
Title
Lie Group Symmetry Discovery and Enforcement Using Vector Fields
Lie Group Symmetry Discovery and Enforcement Using Vector Fields
Ben Shaw
Sasidhar Kunapuli
Abram Magner
Kevin R. Moon
32
0
0
13 May 2025
Surrogate to Poincaré inequalities on manifolds for dimension reduction in nonlinear feature spaces
Surrogate to Poincaré inequalities on manifolds for dimension reduction in nonlinear feature spaces
Anthony Nouy
Alexandre Pasco
42
0
0
03 May 2025
SPD Learning for Covariance-Based Neuroimaging Analysis: Perspectives, Methods, and Challenges
SPD Learning for Covariance-Based Neuroimaging Analysis: Perspectives, Methods, and Challenges
Ce Ju
Reinmar J. Kobler
Antoine Collas
M. Kawanabe
Cuntai Guan
Bertrand Thirion
43
0
0
26 Apr 2025
Riemannian Optimization on Relaxed Indicator Matrix Manifold
Riemannian Optimization on Relaxed Indicator Matrix Manifold
Jinghui Yuan
Fangyuan Xie
Feiping Nie
Xuelong Li
75
0
0
26 Mar 2025
Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices
Wrapped Gaussian on the manifold of Symmetric Positive Definite Matrices
Thibault de Surrel
Fabien Lotte
Sylvain Chevallier
Florian Yger
60
0
0
03 Feb 2025
Manifold learning and optimization using tangent space proxies
Manifold learning and optimization using tangent space proxies
Ryan A. Robinett
Lorenzo Orecchia
Samantha J. Riesenfeld
43
0
0
22 Jan 2025
Residual Deep Gaussian Processes on Manifolds
Residual Deep Gaussian Processes on Manifolds
Kacper Wyrwal
Andreas Krause
Viacheslav Borovitskiy
BDL
49
0
0
31 Oct 2024
A Metric-based Principal Curve Approach for Learning One-dimensional Manifold
A Metric-based Principal Curve Approach for Learning One-dimensional Manifold
E. Cui
18
0
0
20 May 2024
Cluster Exploration using Informative Manifold Projections
Cluster Exploration using Informative Manifold Projections
Stavros Gerolymatos
Xenophon Evangelopoulos
V. Gusev
John Y. Goulermas
14
0
0
26 Sep 2023
A survey of manifold learning and its applications for multimedia
A survey of manifold learning and its applications for multimedia
Hannes Fassold
39
1
0
08 Sep 2023
Towards frugal unsupervised detection of subtle abnormalities in medical
  imaging
Towards frugal unsupervised detection of subtle abnormalities in medical imaging
Geoffroy Oudoumanessah
Carole Lartizien
M. Dojat
Florence Forbes
MedIm
25
2
0
04 Sep 2023
Gradient-Based Spectral Embeddings of Random Dot Product Graphs
Gradient-Based Spectral Embeddings of Random Dot Product Graphs
Marcelo Fiori
Bernardo Marenco
Federico Larroca
P. Bermolen
Gonzalo Mateos
BDL
27
3
0
25 Jul 2023
Alignment of Density Maps in Wasserstein Distance
Alignment of Density Maps in Wasserstein Distance
A. Singer
Ruiyi Yang
30
8
0
21 May 2023
Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies
Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies
Hanna Ziesche
Leonel Rozo
26
5
0
17 May 2023
Faster Riemannian Newton-type Optimization by Subsampling and Cubic
  Regularization
Faster Riemannian Newton-type Optimization by Subsampling and Cubic Regularization
Yian Deng
Tingting Mu
21
1
0
22 Feb 2023
A Survey of Geometric Optimization for Deep Learning: From Euclidean
  Space to Riemannian Manifold
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold
Yanhong Fei
Xian Wei
Yingjie Liu
Zhengyu Li
Mingsong Chen
28
6
0
16 Feb 2023
Riemannian Optimization for Variance Estimation in Linear Mixed Models
Riemannian Optimization for Variance Estimation in Linear Mixed Models
L. Sembach
J. P. Burgard
Volker Schulz
4
0
0
18 Dec 2022
A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant
  Analysis
A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant Analysis
Dong Min Roh
Z. Bai
Ren-Cang Li
13
1
0
21 Nov 2022
Finding the global semantic representation in GAN through Frechet Mean
Finding the global semantic representation in GAN through Frechet Mean
Jaewoong Choi
Geonho Hwang
Hyunsoo Cho
Myung-joo Kang
GAN
DRL
19
2
0
11 Oct 2022
Stochastic Neuromorphic Circuits for Solving MAXCUT
Stochastic Neuromorphic Circuits for Solving MAXCUT
Bradley H. Theilman
Yipu Wang
Ojas D. Parekh
William M. Severa
John Smith
J. Aimone
11
7
0
05 Oct 2022
Manifold Free Riemannian Optimization
Manifold Free Riemannian Optimization
B. Shustin
H. Avron
B. Sober
31
2
0
07 Sep 2022
Human-to-Robot Manipulability Domain Adaptation with Parallel Transport
  and Manifold-Aware ICP
Human-to-Robot Manipulability Domain Adaptation with Parallel Transport and Manifold-Aware ICP
Anna Reithmeir
Luis F. C. Figueredo
Sami Haddadin
16
5
0
16 Aug 2022
SPD domain-specific batch normalization to crack interpretable
  unsupervised domain adaptation in EEG
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
Reinmar J. Kobler
J. Hirayama
Qibin Zhao
M. Kawanabe
19
53
0
02 Jun 2022
Riemannian Hamiltonian methods for min-max optimization on manifolds
Riemannian Hamiltonian methods for min-max optimization on manifolds
Andi Han
Bamdev Mishra
Pratik Jawanpuria
Pawan Kumar
Junbin Gao
35
17
0
25 Apr 2022
Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN
  Design
Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design
Xiran Fan
Chun-Hao Yang
B. Vemuri
37
18
0
03 Dec 2021
Riemannian Functional Map Synchronization for Probabilistic Partial
  Correspondence in Shape Networks
Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks
Faria Huq
Adrish Dey
Sahra Yusuf
Dena Bazazian
Tolga Birdal
Nina Miolane
41
1
0
29 Nov 2021
NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in
  Machine Learning
NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning
Buyun Liang
Tim Mitchell
Ju Sun
17
3
0
27 Nov 2021
Geometry-aware Bayesian Optimization in Robotics using Riemannian
  Matérn Kernels
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
Noémie Jaquier
Viacheslav Borovitskiy
A. Smolensky
Alexander Terenin
Tamim Asfour
Leonel Rozo
29
35
0
02 Nov 2021
Topologically Regularized Data Embeddings
Topologically Regularized Data Embeddings
R. Vandaele
Bo Kang
Jefrey Lijffijt
T. D. Bie
Yvan Saeys
18
9
0
18 Oct 2021
Simulation-based Bayesian inference for multi-fingered robotic grasping
Simulation-based Bayesian inference for multi-fingered robotic grasping
Norman Marlier
O. Bruls
Gilles Louppe
31
6
0
29 Sep 2021
Nonlinear matrix recovery using optimization on the Grassmann manifold
Nonlinear matrix recovery using optimization on the Grassmann manifold
Florentin Goyens
C. Cartis
Armin Eftekhari
26
6
0
13 Sep 2021
Interactive Dimensionality Reduction for Comparative Analysis
Interactive Dimensionality Reduction for Comparative Analysis
Takanori Fujiwara
Xinhai Wei
Jian Zhao
K. Ma
27
30
0
29 Jun 2021
Faster Randomized Methods for Orthogonality Constrained Problems
Faster Randomized Methods for Orthogonality Constrained Problems
B. Shustin
H. Avron
16
2
0
22 Jun 2021
On Riemannian Optimization over Positive Definite Matrices with the
  Bures-Wasserstein Geometry
On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry
Andi Han
Bamdev Mishra
Pratik Jawanpuria
Junbin Gao
11
38
0
01 Jun 2021
The Complex-Step Derivative Approximation on Matrix Lie Groups
The Complex-Step Derivative Approximation on Matrix Lie Groups
C. C. Cossette
A. Walsh
James Richard Forbes
17
18
0
06 May 2021
A Riemannian Newton Trust-Region Method for Fitting Gaussian Mixture
  Models
A Riemannian Newton Trust-Region Method for Fitting Gaussian Mixture Models
L. Sembach
J. P. Burgard
Volker Schulz
8
3
0
30 Apr 2021
Bayesian Quadrature on Riemannian Data Manifolds
Bayesian Quadrature on Riemannian Data Manifolds
Christian Frohlich
A. Gessner
Philipp Hennig
Bernhard Schölkopf
Georgios Arvanitidis
29
4
0
12 Feb 2021
McTorch, a manifold optimization library for deep learning
McTorch, a manifold optimization library for deep learning
Mayank Meghwanshi
Pratik Jawanpuria
Anoop Kunchukuttan
Hiroyuki Kasai
Bamdev Mishra
AI4CE
12
41
0
03 Oct 2018
geomstats: a Python Package for Riemannian Geometry in Machine Learning
geomstats: a Python Package for Riemannian Geometry in Machine Learning
Nina Miolane
Johan Mathe
Claire Donnat
Mikael Jorda
Xavier Pennec
AI4CE
37
123
0
21 May 2018
Optimal projection of observations in a Bayesian setting
Optimal projection of observations in a Bayesian setting
L. Giraldi
O. Maître
Ibrahim Hoteit
Omar Knio
22
8
0
19 Sep 2017
ManifoldOptim: An R Interface to the ROPTLIB Library for Riemannian
  Manifold Optimization
ManifoldOptim: An R Interface to the ROPTLIB Library for Riemannian Manifold Optimization
Sean Martin
Andrew M. Raim
Wen Huang
K. Adragni
14
13
0
12 Dec 2016
MERLiN: Mixture Effect Recovery in Linear Networks
MERLiN: Mixture Effect Recovery in Linear Networks
S. Weichwald
Moritz Grosse-Wentrup
Arthur Gretton
CML
25
7
0
03 Dec 2015
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