Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression
Authors: Bae Seong Park, Se Jung Kwon, Daehwan Oh, Byeongwook Kim, Dongsoo Lee
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that our proposed compression scheme achieves almost the maximum compression ratio for the Transformer and Res Net-50 pruned by various fine-grained pruning methods. [...] In this section, we demonstrate the encoding capability of our proposed sequential encoding techniques using synthetic random data and NNs pruned by various pruning methods. |
| Researcher Affiliation | Industry | 1NAVER CLOVA, EMAIL 2Samsung Research, EMAIL |
| Pseudocode | Yes | Algorithm 1: SpMV (CSR format) [...] Algorithm 2: Proposed SpMV (using encoded weights) [...] Algorithm 3: Encoding algorithm when Ns = 2. |
| Open Source Code | No | The paper mentions a link (https://github.com/google-research/google-research/tree/master/state_of_sparsity) which is for models used in their experiments, not for the source code of their proposed encoding methodology. |
| Open Datasets | Yes | We measure compression capability of our proposed sequential encoding scheme using sparse Transformer (Vaswani et al., 2017) on WMT 14 en-de dataset and Res Net-50 (He et al., 2016) on Image Net. |
| Dataset Splits | No | The paper uses the WMT 14 en-de dataset and ImageNet but does not explicitly provide information regarding specific train, validation, or test splits for these datasets. |
| Hardware Specification | Yes | MKL library (operated by i7-7700 @ 3.6GHz) and CUDA 10.2 library (performed by n VIDIA V100) perform sparse matrix multiplications whose execution times are normalized with respect to corresponding dense matrix multiplications (i.e., using a dense (2048 2048) matrix). |
| Software Dependencies | Yes | MKL library (operated by i7-7700 @ 3.6GHz) and CUDA 10.2 library (performed by n VIDIA V100) perform sparse matrix multiplications whose execution times are normalized with respect to corresponding dense matrix multiplications (i.e., using a dense (2048 2048) matrix). |
| Experiment Setup | Yes | For our experiments, Nin is selected to be 8 such that we feed a decoder on a byte-level. [...] Specifically, for a given set of Nin and Nout, an element of M RNout ((Ns+1) Nin) is randomly assigned to 0 or 1 with equal probability. [...] For the Res Net-50 model (on Image Net), we also consider signed INT8 format. |