Notice: 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. Full accuracy metrics and methodology are described in [1].
The Product Cut
Authors: Thomas Laurent, James von Brecht, Xavier Bresson, arthur szlam
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude with an experimental evaluation and comparison of the algorithm on real world data sets to validate these claims. |
| Researcher Affiliation | Collaboration | Xavier Bresson Nanyang Technological University Singapore EMAIL Thomas Laurent Loyola Marymount University Los Angeles EMAIL Arthur Szlam Facebook AI Research New York EMAIL James H. von Brecht California State University, Long Beach Long Beach EMAIL |
| Pseudocode | Yes | Algorithm 1 Randomized SLP for PCut |
| Open Source Code | Yes | 1The code is available at https://github.com/xbresson/pcut |
| Open Datasets | Yes | We provide experimental results on four text data sets (20NEWS, RCV1, WEBKB4, CITESEER) and four data sets containing images of handwritten digits (MNIST, PENDIGITS, USPS, OPTDIGITS). |
| Dataset Splits | No | The paper uses datasets for evaluation but does not specify training, validation, or test splits. |
| Hardware Specification | No | The paper states that experiments were performed on the 'same architecture' but does not provide specific hardware details (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper refers to various algorithms and methods (e.g., NCut, NMFR, Algebraic Multigrid) but does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | Yes | For the PCut algorithm, we use α = .9 when defining Ωα. Also, in order to illustrate the tradeoff when selecting the rate at which the number of enforced constraints sk increases, we report accuracy results for the linear rates sk = 10 4 n := λ1 and sk = 5 10 4 n := λ2 where n denotes the total number of vertices in the data set. |