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. 2103.00899
  4. Cited By
Computationally Efficient Wasserstein Loss for Structured Labels

Computationally Efficient Wasserstein Loss for Structured Labels

1 March 2021
Ayato Toyokuni
Sho Yokoi
H. Kashima
M. Yamada
ArXiv (abs)PDFHTML

Papers citing "Computationally Efficient Wasserstein Loss for Structured Labels"

2 / 2 papers shown
Title
An Empirical Study of Self-supervised Learning with Wasserstein Distance
An Empirical Study of Self-supervised Learning with Wasserstein Distance
Makoto Yamada
Yuki Takezawa
Guillaume Houry
Kira Michaela Dusterwald
Deborah Sulem
Han Zhao
Yao-Hung Hubert Tsai
62
1
0
16 Oct 2023
Meta Optimal Transport
Meta Optimal Transport
Brandon Amos
Samuel N. Cohen
Giulia Luise
I. Redko
OT
105
24
0
10 Jun 2022
1