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..
Mixed Precision DNNs: All you need is a good parametrization
Authors: Stefan Uhlich, Lukas Mauch, Fabien Cardinaux, Kazuki Yoshiyama, Javier Alonso Garcia, Stephen Tiedemann, Thomas Kemp, Akira Nakamura
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We confirm our findings with experiments on CIFAR-10 and Image Net and we obtain mixed precision DNNs with learned quantization parameters, achieving state-of-the-art performance. |
| Researcher Affiliation | Industry | Stefan Uhlich , Lukas Mauch , Fabien Cardinaux , Kazuki Yoshiyama Javier Alonso García, Stephen Tiedemann, Thomas Kemp Sony Europe B.V., Germany EMAIL Akira Nakamura Sony Corporate, Japan EMAIL |
| Pseudocode | Yes | The following code gives our differentiable quantizer implementation in NNabla (Sony). The source code for reproducing our results will be published after the review process has been finished. |
| Open Source Code | No | The source code for reproducing our results will be published after the review process has been finished. |
| Open Datasets | Yes | We confirm our findings with experiments on CIFAR-10 and Image Net. |
| Dataset Splits | Yes | Fig. 4 shows the evolution of the training and validation error during training for the case of uniform quantization. The plots for power-of-two quantization can be found in the appendix (Fig. 10). We initialize this network from random parameters or from a pre-trained float network. |
| Hardware Specification | Yes | Each epoch takes about 2.5 min on a single GTX 1080 Ti. |
| Software Dependencies | No | The following code gives our differentiable quantizer implementation in NNabla (Sony). |
| Experiment Setup | Yes | The quantized DNNs are trained for 160 epochs, using SGD with momentum 0.9 and a learning rate schedule starting with 0.01 and reducing it by a factor of 10 after 80 and 120 epochs, respectively. We use random flips and crops for data augmentation. |