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.
Compressive Spectral Clustering
Authors: Nicolas Tremblay, Gilles Puy, Remi Gribonval, Pierre Vandergheynst
ICML 2016 | Venue PDF | LLM Run Details | Input Tokens: 19,640 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,154 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 test the performance of our method on artificial and real-world network data. |
| Researcher Affiliation | Collaboration | INRIA Rennes Bretagne Atlantique, Campus de Beaulieu, FR-35042 Rennes Cedex, France Institute of Electrical Engineering, Ecole Polytechnique F ed erale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland Technicolor, 975 Avenue des Champs Blancs, 35576 Cesson-S evign e, France |
| Pseudocode | Yes | Algorithm 1 Spectral Clustering (Ng et al., 2002) |
| Open Source Code | Yes | All our results are reproducible with the CSCbox downloadable at http:// cscbox.gforge.inria.fr/. |
| Open Datasets | Yes | We first perform well-controlled experiments on the Stochastic Block Model (SBM)... |
| Dataset Splits | No | No explicit details on train/validation/test dataset splits are provided for either the synthetic or real-world datasets. |
| Hardware Specification | Yes | Experiments were done on a laptop with a 2.60 GHz Intel i7 dual-core processor running OS Fedora release 22 with 16 GB of RAM. |
| Software Dependencies | Yes | Implementation was done in Matlab R2015a... The fast filtering part of CSC uses the gsp cheby op function of the GSP toolbox (Perraudin et al., 2014). |
| Experiment Setup | Yes | Input: The Laplacian matrix L, the number of clusters k; and parameters typically set to n = 2k log k, d = 4 log n, p = 50 and γ = 10 3. |