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..
Demystifying and Generalizing BinaryConnect
Authors: Tim Dockhorn, Yaoliang Yu, Eyyüb Sari, Mahdi Zolnouri, Vahid Partovi Nia
NeurIPS 2021 | Venue PDF | 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. |