Demystifying and Generalizing BinaryConnect
Authors: Tim Dockhorn, Yaoliang Yu, Eyyüb Sari, Mahdi Zolnouri, Vahid Partovi Nia
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on CIFAR-10 and Image Net, and verify that PC achieves competitive performance. |
| Researcher Affiliation | Collaboration | Tim Dockhorn University of Waterloo Yaoliang Yu University of Waterloo Eyyüb Sari Huawei Noah s Ark Lab Mahdi Zolnouri Huawei Noah s Ark Lab Vahid Partovi Nia Huawei Noah s Ark Lab |
| Pseudocode | No | The paper describes algorithms using mathematical notation and textual descriptions but does not include formal pseudocode blocks or algorithm listings with specific labels like 'Algorithm 1'. |
| Open Source Code | No | We will provide the code once it has been approved by our internal process. |
| Open Datasets | Yes | We conduct experiments on CIFAR-10 [24] using Res Net20 and Res Net56 [19] (...) We perform a small study on Image Net [12] using Res Net18 [19]. |
| Dataset Splits | No | The paper mentions using CIFAR-10 and ImageNet but does not explicitly detail the training/validation/test dataset splits (e.g., percentages or sample counts) or how a validation set was used, beyond mentioning test accuracy. |
| Hardware Specification | Yes | We conduct experiments on a single NVIDIA Quadro P6000 GPU. |
| Software Dependencies | No | The paper mentions PyTorch but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | we increase the parameter ρ (or equivalently ϱ) linearly: ρt = (1 + t/B) ρ0. In contrast to Bai et al. [5], however, we increase ρ after every gradient step rather than after every epoch as this is more in line with our analysis. We treat ρ0 as a hyperparameter for which we conduct a small grid search. |