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..
Reversible Architectures for Arbitrarily Deep Residual Neural Networks
Authors: Bo Chang, Lili Meng, Eldad Haber, Lars Ruthotto, David Begert, Elliot Holtham
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the efficacy of our architectures against several strong baselines on CIFAR-10, CIFAR-100 and STL-10 with superior or on-par state-of-the-art performance. |
| Researcher Affiliation | Collaboration | 1University of British Columbia, Vancouver, Canada. (EMAIL, EMAIL, EMAIL) 2Xtract Technologies Inc., Vancouver, Canada. (EMAIL, EMAIL) 3Emory University, Atlanta, USA. (EMAIL). |
| Pseudocode | No | The paper does not contain explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a link to source code or an explicit statement about releasing it. |
| Open Datasets | Yes | The CIFAR-10 dataset (Krizhevsky and Hinton 2009) consists of 50,000 training images and 10,000 testing images in 10 classes with 32 32 image resolution. |
| Dataset Splits | No | The paper specifies training and test set sizes but does not explicitly mention a separate validation set split or its size. |
| Hardware Specification | Yes | The CIFAR-10/100 and STL-10 experiments are evaluated on a desktop with an Intel Quad-Core i5 CPU and a single Nvidia 1080 Ti GPU. |
| Software Dependencies | No | The paper mentions 'TensorFlow library' but does not specify a version number. |
| Experiment Setup | Yes | The learning rate is initialized to be 0.1 and decayed by a factor of 10 at 80, 120 and 160 training epochs. The total training step is 80K. The weight decay constant is set to 2 10 4, weight smoothness decay is 2 10 4 and the momentum is set to 0.9. |