# Stochastic Gradient Optimization Techniques

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/stochastic-gradient-optimization-techniques-2/

## Facts

| Field | Value |
| --- | --- |
| Citations | 357,432 |
| Description | This cluster of papers focuses on the application of optimization methods in machine learning, particularly in the context of stochastic gradient descent, random projections, deep learning, convex optimization, matrix decompositions, and large-scale optimization. The papers explore various algorithms and techniques for improving the efficiency and effectiveness of machine learning models, with a specific emphasis on neural networks and generalization. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | https://openalex.org/T11612 |
| Works | 29,701 |

## Topic researchers

Showing 12 of 20.

- [Yoshua Bengio](https://scholariq.org/researchers/yoshua-bengio/)
- [Geoffrey E. Hinton](https://scholariq.org/researchers/geoffrey-e-hinton/)
- [Robert Tibshirani](https://scholariq.org/researchers/robert-tibshirani/)
- [Trevor Hastie](https://scholariq.org/researchers/trevor-hastie/)
- [Yann LeCun](https://scholariq.org/researchers/yann-lecun/)
- [Ilya Sutskever](https://scholariq.org/researchers/ilya-sutskever/)
- [Michael I. Jordan](https://scholariq.org/researchers/michael-i-jordan/)
- [Oriol Vinyals](https://scholariq.org/researchers/oriol-vinyals/)
- [H. Vincent Poor](https://scholariq.org/researchers/h-vincent-poor/)
- [Alex Krizhevsky](https://scholariq.org/researchers/alex-krizhevsky/)
- [Stephen Boyd](https://scholariq.org/researchers/stephen-boyd/)
- [Jay B. Dean](https://scholariq.org/researchers/jay-b-dean/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.