Methods › General › Information Bottleneck › Aggregated Learning
Aggregated Learning
Introduced by Masoumeh Soflaei et al. in Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Aggregated Learning (AgrLearn) is a vector-quantization approach to learning neural network classifiers. It builds on an equivalence between IB learning and IB quantization and exploits the power of vector quantization, which is well known in information theory.
Papers archive 2025-07-28
1 shown of 1, 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.
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Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers 12 Jan 2020 · 1 repository · arXiv:2001.03955
Tasks archive 2025-07-28
6 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Classification | 1 |
| General Classification | 1 |
| Quantization | 1 |
| Representation Learning | 1 |
| Text Classification | 1 |
| text-classification | 1 |
Usage over time archive 2025-07-28
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
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