Learning the Irreducible Representations of Commutative Lie Groups
Authors: Taco Cohen, Max Welling
ICML 2014 | Conference PDF | Archive PDF | Plain Text | 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. |