Methods › General › Stochastic Optimization › Gradient Checkpointing

Gradient Checkpointing

14 papers tagged archive 2025-07-28

Introduced by Tianqi Chen et al. in Training Deep Nets with Sublinear Memory Cost

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

Gradient Checkpointing is a method used for reducing the memory footprint when training deep neural networks, at the cost of having a small increase in computation time.

PaperSource

Papers archive 2025-07-28

14 shown of 14, 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 29 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
GPU7
Computational Efficiency2
Image Classification2
All1
Classification1
Contrastive Learning1
Diversity1
Form1
Image Captioning1
Language Modeling1
Language Modelling1
MRI Reconstruction1
Machine Translation1
Mamba1
Model Optimization1
Model Selection1
Multi-class Classification1
Music Generation1
Scheduling1
Self-Supervised Image Classification1

Usage over time archive 2025-07-28

Papers per year tagged with Gradient Checkpointing: 2016 to 2024, peak 5 5 0 2016: 1 paper 2016 2017: 0 papers 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 0 papers 2020 2021: 3 papers 2021 2022: 1 paper 2022 2023: 4 papers 2023 2024: 5 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (14 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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