Research map / Architectures and efficiency / Pruning and efficient tuning
A Probabilistic Approach to Neural Network Pruning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Prune the smallest ⌊D_k^{1-α}⌋ weights in each hidden layer | Target FCN · Unit ball | uniform output error: ≤ ε | No clear effect |
| Randomly prune ⌊D_k^{1-α}⌋ weights in each hidden layer | Target FCN · Unit ball | uniform output error: ≤ ε | No clear effect |
| Randomly prune ⌊d^{2-α}⌋ filters in each convolutional layer | Target CNN · Unit cube | uniform output error: ≤ p^{-β₁} L^{l-1} p₀ √d [p^{-β₁}(p^{-β₁}+d^{-β₂})^{l-2} - p^{-(l-1)β₁}] | No clear effect |
Closest papers by meaning
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- “Learning-Compression” Algorithms for Neural Net Pruning (CVPR 2018 · similarity 0.91)
- Rethinking the Pruning Criteria for Convolutional Neural Network (NeurIPS 2021 · similarity 0.91)
- How Sparse Can We Prune A Deep Network: A Fundamental Limit Perspective (NeurIPS 2024 · similarity 0.90)
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