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..
Robust Implicit Networks via Non-Euclidean Contractions
Authors: Saber Jafarpour, Alexander Davydov, Anton Proskurnikov, Francesco Bullo
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate our framework in image classification through the MNIST and the CIFAR-10 datasets. Our numerical results demonstrate improved accuracy and robustness of the implicit models with smaller input-output Lipschitz bounds. |
| Researcher Affiliation | Academia | 1 Center for Control, Dynamical Systems and Computation, University of California, Santa Barbara, 93106-5070, USA, EMAIL. 2 Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy; 3 Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, St. Petersburg, Russia, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github. com/davydovalexander/Non-Euclidean_Mon_Op_Net. |
| Open Datasets | Yes | Finally, we evaluate our framework in image classification through the MNIST and the CIFAR-10 datasets. In the digit classification dataset MNIST... In the image classification dataset CIFAR-10... |
| Dataset Splits | No | The paper mentions 60000 training images and 10000 test images for MNIST, and 50000 training images and 10000 test images for CIFAR-10, but does not specify a separate validation set or its split details. |
| Hardware Specification | Yes | All models were trained using Google Colab with a Tesla P100-PCIE-16GB GPU. |
| Software Dependencies | No | The paper does not explicitly mention specific software dependencies with version numbers. |
| Experiment Setup | Yes | All models are of order n = 100, used the Re LU activation function φi(x) = (x)+, and are trained with a batch size of 300 over 10 epochs with a learning rate of 1.5 10 2. We train both models with a batch size of 256 and a learning rate of 10 3 for 40 epochs. |