arXiv (All)
open access
Stochastic Gradient Descent with Momentum is Algorithmically Stable
Stochastic gradient descent with momentum (SGDM) is one of the most widely used optimization algorithms in machine learning. While optimization properties of SGDM have been extensively studied in the literature, it remains insufficiently understood whether and when SGDM can generalize well to unseen
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Perfect Parallelization in Mini-Batch SGD with Classical Momentum Acceleration#200500
- Adaptive Schauder Stochastic Mirror Descent in Banach Spaces#620453
- Adaptive Schauder Stochastic Mirror Descent in Banach Spaces#618774
- Structured Spectral Step-Sizes and Rebound-Aware Ordering for Gradient Methods#1064904
- Momentum-based gradient descent methods for Lie groups#971656
Record · ID 1142700
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive