Deep learning· 2010
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
On the reading lists
- #372 in the Top 500 (2026 edition)
- #42 in Deep learning (2026 edition)
Why it matters
Introduced per-parameter adaptive gradient scaling, influencing RMSProp, Adam, and subsequent optimizers.
Condensed from the expert lists' notes.
The record
- First released
- 2010 (from OpenAlex)
- Published
- 2011
- Citations
- 11,918 citations, as counted by
Semantic Scholar (non-commercial licence) - Named by
- both expert lists
- Score
- 0.396 · computed rank 372 · consensus 0.48, must-read 0, citations percentile 0.721 (2010s · deep-learning), awards 0, existing lists 0. Method
Data sources
Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0), and
Semantic Scholar (used under its non-commercial API licence). Each count names its source. See the method.