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Sparse Autoencoder

57 papers tagged archive 2025-07-28

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

A Sparse Autoencoder is a type of autoencoder that employs sparsity to achieve an information bottleneck. Specifically the loss function is constructed so that activations are penalized within a layer. The sparsity constraint can be imposed with L1 regularization or a KL divergence between expected average neuron activation to an ideal distribution p.

Image: Jeff Jordan. Read his blog post (click) for a detailed summary of autoencoders.

Papers archive 2025-07-28

30 shown of 57, 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 78 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
Classification4
Denoising4
General Classification4
Dictionary Learning3
Language Modeling3
Language Modelling3
Representation Learning3
regression3
Anomaly Detection2
Clustering2
Decoder2
Diagnostic2
Dimensionality Reduction2
EEG2
Electroencephalogram (EEG)2
Generative Adversarial Network2
Image Classification2
Large Language Model2
Small Data Image Classification2
Transfer Learning2

Usage over time archive 2025-07-28

Papers per year tagged with Sparse Autoencoder: 2011 to 2025, peak 22 22 0 2011: 1 paper 2011 2012: 0 papers 2013: 0 papers 2013 2014: 0 papers 2015: 2 papers 2015 2016: 2 papers 2017: 1 paper 2017 2018: 3 papers 2019: 5 papers 2019 2020: 2 papers 2021: 1 paper 2021 2022: 2 papers 2023: 4 papers 2023 2024: 12 papers 2025: 22 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (57 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

Generative Models

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