MeLLoC: Lossless Compression with High-order Mechanism Learning
Authors: Xinyue Luo, Jin Cheng, Yu Chen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on various scientific datasets, Me LLo C consistently outperforms state-of-the-art lossless compressors while offering compelling trade-offs between compression ratios and computational costs. |
| Researcher Affiliation | Academia | Xinyue Luo1 Jin Cheng1,2 Yu Chen2 1School of Mathematical Sciences, Fudan University 2School of Mathematics, Shanghai University of Finance and Economics |
| Pseudocode | Yes | Algorithm 1 Compression based on optimizing source term |
| Open Source Code | No | The code is not publicly available due to restrictions of an ongoing commercial project. Despite this, the authors are open to academic collaboration, offering an avenue for interested researchers to engage in further communications. |
| Open Datasets | Yes | This section presents the experimental results of applying our proposed lossless compression algorithm to the CESM-ATM and Hurricane datasets from the SDRBench[18]. |
| Dataset Splits | No | The proposed method does not involve neural networks or a training process, and therefore does not specify traditional training/validation splits. |
| Hardware Specification | Yes | All tests were conducted on a Mac with M1 Silicon, mac OS 14.1.2, 16GB RAM. |
| Software Dependencies | No | The paper does not explicitly provide specific software dependencies with version numbers used for the implementation of Me LLo C. |
| Experiment Setup | Yes | We optimize n for coefficients Ci by starting with high precision and gradually reducing it while monitoring reconstruction error and compression ratio. |