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..
Towards the Limit of Network Quantization
Authors: Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, using the simple uniform quantization followed by Huffman coding, we show from our experiments that the compression ratios of 51.25, 22.17 and 40.65 are achievable for Le Net, 32-layer Res Net and Alex Net, respectively. |
| Researcher Affiliation | Industry | Yoojin Choi, Mostafa El-Khamy, and Jungwon Lee Samsung US R&D Center, San Diego, CA 92121, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Iterative solution for entropy-constrained network quantization |
| Open Source Code | No | The paper does not provide any links to source code repositories, nor does it state that its code is publicly available or in supplementary materials. |
| Open Datasets | Yes | Le Net (Le Cun et al., 1998) for the MNIST data set, (b) Res Net (He et al., 2015) for the CIFAR-10 data set, and (c) Alex Net (Krizhevsky et al., 2012) for the Image Net ILSVRC-2012 data set (Russakovsky et al., 2015). |
| Dataset Splits | Yes | For the data set X used to compute Hessian in (8), we can either reuse a training data set or use some other data set, e.g., validation data set. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, memory, or cloud computing instances used for the experiments. |
| Software Dependencies | No | The paper mentions using a 'pre-trained Alex Net Caffe model' and refers to the 'Adam SGD optimizer (Kingma & Ba, 2014)' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For Hessian computation, 50,000 samples of the training set are reused. We also evaluate the performance when Hessian is computed with 1,000 samples only. |