Manifold Structured Prediction
Authors: Alessandro Rudi, Carlo Ciliberto, GianMaria Marconi, Lorenzo Rosasco
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Promising experimental results on both simulated and real data complete our study. |
| Researcher Affiliation | Academia | 1INRIA Département d informatique, École Normale Supérieure PSL Research University, Paris, France. 2Department of Electrical and Electronic Engineering, Imperial College, London, UK. 3Università degli studi di Genova & Istituto Italiano di Tecnologia, Genova, Italy. 4Massachusetts Institute of Technology, Cambridge, USA. |
| Pseudocode | No | The paper mentions that 'the algorithm is recalled in ??' (referring to a missing appendix) but does not include a structured pseudocode or algorithm block within the provided text. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We compared the performance of the manifold structured estimator proposed in this paper with the manifold regression approach in [47] on the FVC fingerprint verification challenge dataset3. The footnote refers to http://bias.csr.unibo.it/fvc2004. Also, it mentions 'multilabel benchmark datasets described in [51]' and provides the full citation for [51]. |
| Dataset Splits | Yes | We generated datasets of increasing dimension m from 5 to 50, each with 1000 points for training, 100 for validation and 100 for testing. |
| Hardware Specification | Yes | We gratefully acknowledge the support of NVIDIA Corporation for the donation of the Titan Xp GPUs and the Tesla k40 GPU used for this research. |
| Software Dependencies | No | The paper mentions algorithms and methods (e.g., 'Riemannian Gradient Descent (RGD) algorithm'), but does not provide specific software names along with their version numbers required for replication. |
| Experiment Setup | Yes | The kernel bandwidth σ was chosen and the regularization parameter λ were selected by cross-validation respectively in the ranges 0.1 to 1000 and 10 6 to 1 (logarithmically spaced). |