Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning with Group Invariant Features: A Kernel Perspective.
Authors: Youssef Mroueh, Stephen Voinea, Tomaso A. Poggio
NeurIPS 2015 | Venue PDF | LLM Run Details | Input Tokens: 14,716 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,168 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the validity of these claims on three datasets: text (artificial), vision (MNIST), and speech (TIDIGITS). |
| Researcher Affiliation | Collaboration | Youssef Mroueh IBM Watson Group EMAIL Stephen Voinea CBMM, MIT. EMAIL Tomaso Poggio CBMM, MIT . EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the code for their described methodology. |
| Open Datasets | Yes | MNIST (Figure 2): We seek local invariance to translation and rotation, and so all random templates are translated by up to 3 pixels in all directions and rotated between -20 and 20 degrees. TIDIGITS (Figure 3): We use a subset of TIDIGITS consisting of 326 speakers (men, women, children) reading the digits 0-9 in isolation, and so each datapoint is a waveform of a single word. |
| Dataset Splits | No | The paper mentions 'training set' and 'test points' but does not specify training, validation, and test splits with percentages, absolute counts, or cross-validation details. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | All RLS experiments in this paper were completed with the GURLS toolbox [23]. (No version number provided for GURLS toolbox). |
| Experiment Setup | Yes | RLS will perform the optimization, min W Rm T 1 N ||Y Φ(X)W||2 F + λ||W||2 F , where || ||F is the Frobenius norm, λ is the regularization parameter, and Φ is the feature map, which for the representation described in this paper will be a CDF pooling of the data projected onto group-transformed random templates. [...] Φ = CDF(n, m) refers to a random feature map with n bins and m templates. |