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..
Tensor Switching Networks
Authors: Chuan-Yung Tsai, Andrew M. Saxe, Andrew M. Saxe, David Cox
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate that the TS network is indeed more expressive and consistently learns faster than standard Re LU networks. |
| Researcher Affiliation | Academia | Chuan-Yung Tsai , Andrew Saxe , David Cox Center for Brain Science, Harvard University, Cambridge, MA 02138 EMAIL |
| Pseudocode | No | The paper describes algorithms textually but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code and scripts for reproducing our experiments are available at https://github.com/coxlab/tsnet. |
| Open Datasets | Yes | We adopt 3 datasets, viz. MNIST, CIFAR10 and SVHN2 |
| Dataset Splits | Yes | We adopt 3 datasets, viz. MNIST, CIFAR10 and SVHN2, where we reserve the last 5,000 training images for validation. |
| Hardware Specification | No | The paper mentions 'multicore CPU acceleration' and 'GPU acceleration' but does not specify any particular hardware models (e.g., CPU or GPU types). |
| Software Dependencies | No | The paper mentions software like Matlab, libsvm-compact, Python, Numpy, and Keras, but no specific version numbers for any of these dependencies are provided. |
| Experiment Setup | Yes | For all MLPs and CNNs, we universally use SGD with learning rate 10 3, momentum 0.9, L2 weight decay 10 3 and batch size 128 to reduce the grid search complexity by focusing on architectural hyperparameters. |