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Demon

16 papers tagged archive 2025-07-28

Introduced by John Chen et al. in Demon: Improved Neural Network Training with Momentum Decay

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

Decaying Momentum, or Demon, is a stochastic optimizer motivated by decaying the total contribution of a gradient to all future updates. By decaying the momentum parameter, the total contribution of a gradient to all future updates is decayed. A particular gradient term gₜ contributes a total of η∑ᵢβⁱ of its "energy" to all future gradient updates, and this results in the geometric sum, ∑^∞ᵢ₌₁βⁱ = β∑^∞ᵢ₌₀βⁱ = β/(1-β). Decaying this sum results in the Demon algorithm. Letting βᵢₙᵢₜ be the initial β; then at the current step t with total T steps, the decay routine is given by solving the below for βₜ:

βₜ/(1-βₜ) = (1-t/T)βᵢₙᵢₜ/(1-βᵢₙᵢₜ)

Where (1-t/T) refers to the proportion of iterations remaining. Note that Demon typically requires no hyperparameter tuning as it is usually decayed to $0$ or a small negative value at time T. Improved performance is observed by delaying the decaying. Demon can be applied to any gradient descent algorithm with a momentum parameter.

PaperSourceSee Code · JRC1995/DemonRangerOptimizer

Papers archive 2025-07-28

16 shown of 16, 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

18 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
Graph Neural Network2
Pose Estimation2
Adversarial Attack1
Atari Games1
Autonomous Driving1
Camera Pose Estimation1
Denoising1
Edge-computing1
Image Classification1
Image Generation1
Language Modeling1
Language Modelling1
Scene Understanding1
Self-Supervised Learning1
Stochastic Optimization1
Text to Image Generation1
Text-to-Image Generation1
image-classification1

Usage over time archive 2025-07-28

Papers per year tagged with Demon: 2019 to 2025, peak 5 5 0 2019: 1 paper 2019 2020: 0 papers 2020 2021: 2 papers 2021 2022: 4 papers 2022 2023: 3 papers 2023 2024: 5 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (16 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

Momentum Rules

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