ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2503.18010
  4. Cited By
Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding
v1v2 (latest)

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
ArXiv (abs)PDFHTML

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
Wormhole Loss for Partial Shape Matching
Amit Bracha
Thomas Dagès
Ron Kimmel
69
4
0
30 Oct 2024
Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy
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
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
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
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
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
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
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
Directed Graph Auto-Encoders
Georgios Kollias
Vasileios Kalantzis
Tsuyoshi Idé
A. Lozano
Naoki Abe
BDLGNN
71
36
0
25 Feb 2022
Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach
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
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
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
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
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
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
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
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
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
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
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
Poincaré Embeddings for Learning Hierarchical Representations
Maximilian Nickel
Douwe Kiela
98
1,312
0
22 May 2017
Revisiting Semi-Supervised Learning with Graph Embeddings
Revisiting Semi-Supervised Learning with Graph Embeddings
Zhilin Yang
William W. Cohen
Ruslan Salakhutdinov
GNNSSL
180
2,107
0
29 Mar 2016
word2vec Explained: deriving Mikolov et al.'s negative-sampling
  word-embedding method
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method
Yoav Goldberg
Omer Levy
SSL
81
1,611
0
15 Feb 2014
1