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..
Scale-Equivariant Steerable Networks
Authors: Ivan Sosnovik, Michał Szmaja, Arnold Smeulders
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate state-of-the-art results on the MNIST-scale dataset and on the STL-10 dataset in the supervised learning setting. In this section we conduct the experiments and compare various methods for working with scale variations in input data. |
| Researcher Affiliation | Collaboration | Ivan Sosnovik , Michał Szmaja , Arnold Smeulders Uv A-Bosch Delta Lab University of Amsterdam |
| Pseudocode | No | We illustrate all algorithms in Figure 1. |
| Open Source Code | Yes | Source code is available at http://github.com/isosnovik/sesn |
| Open Datasets | Yes | We conduct the experiments on the MNIST-scale dataset. We rescale the images of the MNIST dataset Le Cun et al. (1998)... |
| Dataset Splits | Yes | The obtained dataset is then split into 10,000 for training, 2,000 for evaluation and 50,000 for testing. |
| Hardware Specification | Yes | We used 1 Nvidia Ge Force GTX 1080Ti GPU for training the models. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | All models are trained with the Adam optimizer Kingma & Ba (2014) for 60 epochs with a batch size of 128. Initial learning rate is set to 0.01 and divided by 10 after 20 and 40 epochs. All models are trained for 1000 epochs with a batch size of 128. We use SGD optimizer with Nesterov momentum of 0.9 and weight decay of 5 10 4. The initial learning rate is set to 0.1 and divided by 5 after 300, 400, 600 and 800 epochs. |