Research map / Architectures and efficiency / Pruning and efficient tuning
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Viterbi-based Pruning for Sparse Matrix with Fixed and High Index Compression Ratio (ICLR 2018 · similarity 0.94)
- Structured Compression by Weight Encryption for Unstructured Pruning and Quantization (CVPR 2020 · similarity 0.90)
- Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio (AAAI 2020 · similarity 0.88)
- Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression (ICLR 2022 · similarity 0.87)
- Weight matrices compression based on PDB model in deep neural networks (ICML 2025 · similarity 0.85)
- Two Sparse Matrices are Better than One: Sparsifying Neural Networks with Double Sparse Factorization (ICLR 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.