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..
Lie Group Decompositions for Equivariant Neural Networks
Authors: Mircea Mironenco, Patrick ForrΓ©
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the robustness and out-of-distribution generalisation capability of our model on the benchmark affine-invariant classification task, outperforming previous proposals. ... For all experiments we use a Res Net-style architecture... |
| Researcher Affiliation | Academia | Mircea Mironenco AI4Science Lab, AMLab Informatics Institute University of Amsterdam EMAIL Patrick Forr e AI4Science Lab, AMLab Informatics Institute University of Amsterdam EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/mirceamironenco/rgenn. |
| Open Datasets | Yes | We evaluate our model on a benchmark affine-invariant image classification task employing the aff NIST dataset2. ... The experimental setup involves training on the standard set of 50000 non-transformed MNIST images (padded to 40 40)... |
| Dataset Splits | No | The paper mentions training and test sets but does not provide specific details about a validation dataset split or how it was used. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions specific software components like "Adam optimizer" and "SIREN networks" but does not provide their specific version numbers. |
| Experiment Setup | Yes | All experiments will use the same Res Net-like architecture He et al. (2016)... We set Ο0 = 10 for all experiments. We use 42 output channels in both the lifting and cross-correlation layers. Each SIREN network consists of 2 layers of size 60. ... The models are trained for 100 epochs, with a batch size of 128, and the Adam optimizer of Kingma & Ba (2014) with a standard learning rate of 0.0001. |