Methods › General › Stochastic Optimization › Gravity

Gravity

202 papers tagged archive 2025-07-28

Introduced by Dariush Bahrami et al. in Gravity Optimizer: a Kinematic Approach on Optimization in Deep Learning

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

Gravity is a kinematic approach to optimization based on gradients.

PaperSource

Papers archive 2025-07-28

30 shown of 202, 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

20 shown of 135 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
Deep Learning7
Object5
Pose Estimation5
Position5
Reinforcement Learning (RL)5
Translation5
Prediction4
Uncertainty Quantification4
3D Reconstruction3
Computational Efficiency3
Data Augmentation3
Decision Making3
Deep Reinforcement Learning3
Form3
Friction3
GPU3
Management3
Operator learning3
State Estimation3
Time Series3

Usage over time archive 2025-07-28

Papers per year tagged with Gravity: 2021 to 2025, peak 58 58 0 2021: 30 papers 2021 2022: 39 papers 2022 2023: 46 papers 2023 2024: 58 papers 2024 2025: 29 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (202 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 Optimization

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