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ADAHESSIAN

AdaHessian

6 papers tagged archive 2025-07-28

Introduced by Zhewei Yao et al. in ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

ADAHESSIAN is a new stochastic optimization algorithm that directly incorporates approximate curvature information from the loss function, and it includes several novel performance-improving features, including a fast Hutchinson based method to approximate the curvature matrix with low computational overhead.

PaperSource

Papers archive 2025-07-28

6 shown of 6, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Stochastic Optimization2
BIG-bench Machine Learning1
Click-Through Rate Prediction1
Deep Learning1
Image Classification1
Second-order methods1
image-classification1

Usage over time archive 2025-07-28

Papers per year tagged with AdaHessian: 2020 to 2024, peak 2 2 0 2020: 1 paper 2020 2021: 1 paper 2021 2022: 1 paper 2022 2023: 1 paper 2023 2024: 2 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (6 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Stochastic OptimizationOptimization

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