ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2309.04788
  4. Cited By
Stochastic Gradient Descent outperforms Gradient Descent in recovering a
  high-dimensional signal in a glassy energy landscape
v1v2 (latest)

Stochastic Gradient Descent outperforms Gradient Descent in recovering a high-dimensional signal in a glassy energy landscape

9 September 2023
Persia Jana Kamali
Pierfrancesco Urbani
ArXiv (abs)PDFHTML

Papers citing "Stochastic Gradient Descent outperforms Gradient Descent in recovering a high-dimensional signal in a glassy energy landscape"

2 / 2 papers shown
Title
Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-dimensional Tokens
Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-dimensional TokensPhysical Review X (PRX), 2024
Vittorio Erba
Emanuele Troiani
Luca Biggio
Antoine Maillard
Lenka Zdeborová
465
2
0
24 Oct 2024
Stochastic Gradient Descent-like relaxation is equivalent to Metropolis
  dynamics in discrete optimization and inference problems
Stochastic Gradient Descent-like relaxation is equivalent to Metropolis dynamics in discrete optimization and inference problemsScientific Reports (Sci Rep), 2023
Maria Chiara Angelini
A. Cavaliere
Raffaele Marino
F. Ricci-Tersenghi
312
5
0
11 Sep 2023
1