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2503.18010
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Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding
23 March 2025
Thomas Dagès
Simon Weber
Ya-Wei Eileen Lin
Ronen Talmon
Daniel Cremers
M. Lindenbaum
A. Bruckstein
Ron Kimmel
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Papers citing
"Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding"
23 / 23 papers shown
Title
Wormhole Loss for Partial Shape Matching
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Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy
Ya-Wei Eileen Lin
Ronald R. Coifman
Zhengchao Wan
Ronen Talmon
190
3
0
28 Oct 2024
Metric Convolutions: A Unifying Theory to Adaptive Convolutions
Thomas Dagès
M. Lindenbaum
A. Bruckstein
94
1
0
08 Jun 2024
DUPLEX: Dual GAT for Complex Embedding of Directed Graphs
Zhaoru Ke
Hang Yu
Jianguo Li
Haipeng Zhang
112
5
0
08 Jun 2024
Finsler-Laplace-Beltrami Operators with Application to Shape Analysis
Simon Weber
Thomas Dagès
Maolin Gao
Daniel Cremers
AI4CE
59
6
0
05 Apr 2024
HoloNets: Spectral Convolutions do extend to Directed Graphs
Christian Koke
Daniel Cremers
101
11
0
03 Oct 2023
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning
Ya-Wei Eileen Lin
Ronald R. Coifman
Zhengchao Wan
Ronen Talmon
92
16
0
30 May 2023
Edge Directionality Improves Learning on Heterophilic Graphs
Emanuele Rossi
Bertrand Charpentier
Francesco Di Giovanni
Fabrizio Frasca
Stephan Günnemann
Michael M. Bronstein
114
69
0
17 May 2023
Directed Graph Auto-Encoders
Georgios Kollias
Vasileios Kalantzis
Tsuyoshi Idé
A. Lozano
Naoki Abe
BDL
GNN
71
36
0
25 Feb 2022
Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach
F. López
Beatrice Pozzetti
Steve J. Trettel
Michael Strube
Anna Wienhard
69
24
0
09 Jun 2021
MagNet: A Neural Network for Directed Graphs
Xitong Zhang
Yixuan He
Nathan Brugnone
Michael Perlmutter
M. Hirn
127
134
0
22 Feb 2021
Generalized Nonlinear and Finsler Geometry for Robotics
Nathan D. Ratliff
Karl Van Wyk
Mandy Xie
Anqi Li
M. A. Rana
AI4CE
71
28
0
28 Oct 2020
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu
Matthias Fey
Marinka Zitnik
Yuxiao Dong
Hongyu Ren
Bowen Liu
Michele Catasta
J. Leskovec
311
2,752
0
02 May 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
568
42,677
0
03 Dec 2019
Multi-scale Attributed Node Embedding
Benedek Rozemberczki
Carl Allen
Rik Sarkar
GNN
273
866
0
28 Sep 2019
Optuna: A Next-generation Hyperparameter Optimization Framework
Takuya Akiba
Shotaro Sano
Toshihiko Yanase
Takeru Ohta
Masanori Koyama
681
5,872
0
25 Jul 2019
Low-dimensional statistical manifold embedding of directed graphs
Thorben Funke
Tian Guo
Alen Lancic
Nino Antulov-Fantulin
61
4
0
24 May 2019
Node Representation Learning for Directed Graphs
Megha Khosla
Jurek Leonhardt
Wolfgang Nejdl
Avishek Anand
58
56
0
22 Oct 2018
Intrinsic Isometric Manifold Learning with Application to Localization
Ariel Schwartz
Ronen Talmon
44
18
0
01 Jun 2018
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Leland McInnes
John Healy
James Melville
205
9,492
0
09 Feb 2018
Poincaré Embeddings for Learning Hierarchical Representations
Maximilian Nickel
Douwe Kiela
98
1,312
0
22 May 2017
Revisiting Semi-Supervised Learning with Graph Embeddings
Zhilin Yang
William W. Cohen
Ruslan Salakhutdinov
GNN
SSL
180
2,107
0
29 Mar 2016
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
Yoav Goldberg
Omer Levy
SSL
81
1,611
0
15 Feb 2014
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