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..
Orthogonalizing Convolutional Layers with the Cayley Transform
Authors: Asher Trockman, J Zico Kolter
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments have two goals: First, we show that our layer remains orthogonal in practice. Second, we compare the performance of our layer versus alternatives (particularly BCOP) on two adversarial robustness tasks on CIFAR-10 |
| Researcher Affiliation | Collaboration | Asher Trockman Computer Science Department Carnegie Mellon University EMAIL J. Zico Kolter Computer Science Department Carnegie Mellon University & Bosch Center for AI EMAIL |
| Pseudocode | Yes | Algorithm 1: Orthogonal convolution via the Cayley transform. |
| Open Source Code | Yes | Code is available at https://github.com/locuslab/orthogonal-convolutions. |
| Open Datasets | Yes | For all experiments, we used CIFAR-10 with standard augmentation, i.e., random cropping and flipping. |
| Dataset Splits | No | The paper mentions using CIFAR-10 but does not specify the training, validation, or test split percentages or sample counts. |
| Hardware Specification | Yes | Width 1 was on a Nvidia RTX 2080 Ti, while 2, 3, 6, and 8 were on a Nvidia Quadro RTX 8000. |
| Software Dependencies | Yes | In Py Torch 1.8, our layer can be implemented as follows. |
| Experiment Setup | Yes | For each architecture/convolution pair, we tried learning rates in {10 5, 10 4, 10 3, 10 2, 10 1}, choosing the one with the best test accuracy. Most often, 0.001 is appropriate. ... Adam (Kingma & Ba, 2014) showed a significant improvement over plain SGD, and we used it for all experiments. ... The batch size was fixed at 128 for all graphs |