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Methods › General › Optimization › Gradient Clipping

Gradient Clipping

167 papers tagged archive 2025-07-28

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

One difficulty that arises with optimization of deep neural networks is that large parameter gradients can lead an SGD optimizer to update the parameters strongly into a region where the loss function is much greater, effectively undoing much of the work that was needed to get to the current solution.

Gradient Clipping clips the size of the gradients to ensure optimization performs more reasonably near sharp areas of the loss surface. It can be performed in a number of ways. One option is to simply clip the parameter gradient element-wise before a parameter update. Another option is to clip the norm ||g|| of the gradient g before a parameter update:

if ||g|| > v then g ←gv/(||g||)

where v is a norm threshold.

Source: Deep Learning, Goodfellow et al

Image Source: Pascanu et al

Papers archive 2025-07-28

30 shown of 167, 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 150 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
Federated Learning12
Language Modelling10
Reinforcement Learning10
Image Classification9
Reinforcement Learning (RL)9
reinforcement-learning9
Deep Learning8
Language Modeling7
GPU6
Privacy Preserving6
Representation Learning6
image-classification6
Stochastic Optimization5
Text Generation5
Deep Reinforcement Learning3
Denoising3
Fairness3
General Classification3
LEMMA3
Large Language Model3

Usage over time archive 2025-07-28

Papers per year tagged with Gradient Clipping: 2015 to 2025, peak 42 42 0 2015: 1 paper 2015 2016: 3 papers 2016 2017: 3 papers 2017 2018: 2 papers 2018 2019: 10 papers 2019 2020: 13 papers 2020 2021: 21 papers 2021 2022: 26 papers 2022 2023: 42 papers 2023 2024: 30 papers 2024 2025: 16 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (167 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

Optimization

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