Methods › General › Learning Rate Schedules › Exponential Decay

Exponential Decay

110 papers tagged archive 2025-07-28

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

Exponential Decay is a learning rate schedule where we decay the learning rate with more iterations using an exponential function:

lr = lr₀exp(-kt)

Image Credit: Suki Lau

Papers archive 2025-07-28

30 shown of 110, 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 107 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
Image Classification12
image-classification7
Neural Architecture Search6
Classification5
Computational Efficiency5
Reinforcement Learning5
Time Series4
Time Series Analysis4
reinforcement-learning4
Graph Neural Network3
Large Language Model3
Multi-agent Reinforcement Learning3
Quantum Machine Learning3
Reinforcement Learning (RL)3
model3
AutoML2
Bayesian Inference2
Change Detection2
Contrastive Learning2
Decision Making2

Usage over time archive 2025-07-28

Papers per year tagged with Exponential Decay: 2014 to 2025, peak 23 23 0 2014: 1 paper 2014 2015: 2 papers 2015 2016: 2 papers 2016 2017: 4 papers 2017 2018: 6 papers 2018 2019: 12 papers 2019 2020: 10 papers 2020 2021: 16 papers 2021 2022: 11 papers 2022 2023: 11 papers 2023 2024: 23 papers 2024 2025: 12 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (110 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

Learning Rate Schedules

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