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..
PowerQuant: Automorphism Search for Non-Uniform Quantization
Authors: Edouard YVINEC, Arnaud Dapogny, Matthieu Cord, Kevin Bailly
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS In this section, we empirically validate our method. First, we discuss the optimization of the exponent parameter a of Power Quant using the reconstruction error, showing its interest as a proxy for the quantized model accuracy from an experimental standpoint. We show that the proposed approach preserves this reconstruction error significantly better, allowing a closer fit to the original weight distribution through non-uniform quantization. Second, we show through a variety of benchmarks that the proposed approach significantly outperforms state-of-the-art data-free methods, thanks to more efficient power function quantization with optimized exponent. Third, we show that the proposed approach comes at a negligible cost in term of inference speed. |
| Researcher Affiliation | Collaboration | Sorbonne Universit e1, CNRS, ISIR, f-75005, 4 Place Jussieu 75005 Paris, France Datakalab2, 114 boulevard Malesherbes, 75017 Paris, France |
| Pseudocode | Yes | Algorithm 1 Weight Quantization Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We validate the proposed Power Quant method on Image Net classification (Deng et al., 2009) ( 1.2M images train/50k test). |
| Dataset Splits | No | The paper states '1.2M images train/50k test' for ImageNet, but does not explicitly provide details for a validation dataset split. |
| Hardware Specification | Yes | Table 17: Inference time, in seconds, over Image Net using batches of size 16 of several networks on a 2070 RTX GPU. |
| Software Dependencies | No | The paper mentions using 'Tensorflow implementations' and 'Numpy library' but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | No | The paper mentions 'batches of size 16' and describes some quantization settings (unsigned integers for activations, symmetric representation for weights, batch-normalization folding), but it does not provide specific hyperparameter values such as learning rate, number of epochs, or optimizer settings. |