IDAP++: Advancing Divergence-Aware Pruning with Joint Filter and Layer Optimization
Summary: IDAP++ proposes divergence-aware neural compression with a unified information-flow metric. It extends pruning from filters to whole layers via joint filter/layer optimization, enabling architecture-agnostic compression across CNNs, transformers, and hybrids. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Aleksei Samarin
- 2. Artem Nazarenko
- 3. Egor Kotenko
- 4. Alexander Savelev
- 5. Aleksei Toropov
- 6. Alexandr Motyko
- 7. Valentin Malykh
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| Rank | Cited Paper | Year | Venue | Pagerank |
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| 333 | Neo: A Learned Query Optimizer | 2019 | VLDB | 0.00027206884 |
| 608 | DeepDB: Learn from Data, not from Queries! | 2020 | VLDB | 0.00019235898 |
| 826 | ALEX: An Updatable Adaptive Learned Index | 2020 | SIGMOD | 0.00016224841 |
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