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. 2303.03027
  4. Cited By
Critical Points and Convergence Analysis of Generative Deep Linear
  Networks Trained with Bures-Wasserstein Loss

Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss

6 March 2023
Pierre Bréchet
Katerina Papagiannouli
Jing An
Guido Montúfar
ArXivPDFHTML

Papers citing "Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss"

3 / 3 papers shown
Title
Spectral Neural Networks: Approximation Theory and Optimization
  Landscape
Spectral Neural Networks: Approximation Theory and Optimization Landscape
Chenghui Li
Rishi Sonthalia
Nicolas García Trillos
19
1
0
01 Oct 2023
Convergence of SGD for Training Neural Networks with Sliced Wasserstein
  Losses
Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses
Eloi Tanguy
19
5
0
21 Jul 2023
Global optimality conditions for deep neural networks
Global optimality conditions for deep neural networks
Chulhee Yun
S. Sra
Ali Jadbabaie
116
117
0
08 Jul 2017
1