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..
Learning the Irreducible Representations of Commutative Lie Groups
Authors: Taco Cohen, Max Welling
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We train the model on pairs of transformed image patches, and show that the learned invariant representation is highly effective for classification. We trained a TSA model with 100 filters on a stream of 250.000 16 16 image patches x(t), y(t). We tested the utility of the model for invariant classification on a rotated version of the MNIST dataset, using a 1-Nearest Neighbor classifier. The results in fig. 4 show that TD outperforms ED, but is outperformed by ˆκ and MD by a large margin. |
| Researcher Affiliation | Academia | Machine Learning Group, University of Amsterdam |
| Pseudocode | No | The paper describes mathematical formulations and derivations but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to code repositories for the described methodology. |
| Open Datasets | Yes | We tested the utility of the model for invariant classification on a rotated version of the MNIST dataset. |
| Dataset Splits | No | The paper mentions "60k training examples and 10k testing examples" but does not specify a separate validation set or detailed split percentages that would allow for full reproduction of data partitioning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers required to replicate the experiment. |
| Experiment Setup | Yes | We trained a TSA model with 100 filters on a stream of 250.000 16 16 image patches x(t), y(t). The learning rate α was initialized at α0 = 0.25 and decayed as α = α0/√T, where T was incremented by one with each pass through the data. Each minibatch consisted of 100 data pairs. We tested the utility of the model for invariant classification on a rotated version of the MNIST dataset, using a 1-Nearest Neighbor classifier. Each digit was rotated by a random angle and rescaled to 16 16 pixels. |