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..
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
Authors: Marco Ciccone, Marco Gallieri, Jonathan Masci, Christian Osendorfer, Faustino Gomez
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show how NAIS-Net exhibits stability in practice, yielding a significant reduction in generalization gap compared to Res Nets. [...] These implementations are compared experimentally with Res Nets on both CIFAR-10 and CIFAR-100 datasets, in section 5, showing that NAIS-Nets achieve comparable classification accuracy with a much better generalization gap. |
| Researcher Affiliation | Collaboration | Marco Ciccone Politecnico di Milano NNAISENSE SA EMAIL Marco Gallieri NNAISENSE SA EMAIL Jonathan Masci NNAISENSE SA EMAIL Christian Osendorfer NNAISENSE SA EMAIL Faustino Gomez NNAISENSE SA EMAIL |
| Pseudocode | Yes | Algorithm 1 Fully Connected Reprojection and Algorithm 2 CNN Reprojection |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | Experiments were conducted comparing NAIS-Net with Res Net, and variants thereof, using both fully-connected (MNIST, section 5.1) and convolutional (CIFAR-10/100, section 5.2) architectures... [27] A. Krizhevsky and G. Hinton. Learning multiple layers of features from tiny images. 2009. [32] Yann Le Cun. The MNIST database of handwritten digits. http://yann.lecun.com/exdb/mnist/, 1998. |
| Dataset Splits | Yes | Experiments were conducted comparing NAIS-Net with Res Net, and variants thereof, using both fully-connected (MNIST, section 5.1) and convolutional (CIFAR-10/100, section 5.2) architectures... For the MNIST dataset [32] a single-block NAIS-Net was compared... These benchmarks are simple enough to allow for multiple runs to test for statistical significance, yet sufficiently complex to require convolutional layers. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'tensorflow/models' in a footnote, but it does not specify version numbers for TensorFlow or any other software libraries or dependencies used in their implementation. |
| Experiment Setup | Yes | All networks were trained using stochastic gradient descent with momentum 0.9 and learning rate 0.1, for 150 epochs. [...] The initial learning rate of 0.1 was decreased by a factor of 10 at epochs 150, 250 and 350 and the experiment were run for 450 epochs. |