Learning with Group Invariant Features: A Kernel Perspective.
Authors: Youssef Mroueh, Stephen Voinea, Tomaso A. Poggio
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 mroueh@us.ibm.com Stephen Voinea CBMM, MIT. voinea@mit.edu Tomaso Poggio CBMM, MIT . tp@ai.mit.edu |
| 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. |