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..
Binarized Neural Networks
Authors: Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate the effectiveness of BNNs, we conducted two sets of experiments on the Torch7 and Theano frameworks. |
| Researcher Affiliation | Academia | (1) Technion, Israel Institute of Technology. (2) Université de Montréal. (3) Columbia University. (4) CIFAR Senior Fellow. |
| Pseudocode | Yes | Algorithm 1: Training a BNN. |
| Open Source Code | Yes | The code for training and running our BNNs is available on-line (both Theano1 and Torch framework2). |
| Open Datasets | Yes | On both, BNNs achieved nearly state-of-the-art results over the MNIST, CIFAR-10 and SVHN datasets. |
| Dataset Splits | Yes | Figure 1: Training curves for different methods on CIFAR-10 dataset. The dotted lines represent the training costs (square hinge losses) and the continuous lines the corresponding validation error rates. |
| Hardware Specification | Yes | The first three columns represent the time it takes to perform a 8192 8192 8192 (binary) matrix multiplication on a GTX750 Nvidia GPU, depending on which kernel is used. |
| Software Dependencies | Yes | Torch7: A matlab-like environment for machine learning. |
| Experiment Setup | No | Implementation details are reported in Appendix A and code for both frameworks is available online. However, these specific details (e.g., hyperparameters like learning rate, batch size) are not provided in the main body of the paper. |