The Product Cut
Authors: Thomas Laurent, James von Brecht, Xavier Bresson, arthur szlam
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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 xavier.bresson@ntu.edu.sg Thomas Laurent Loyola Marymount University Los Angeles tlaurent@lmu.edu Arthur Szlam Facebook AI Research New York aszlam@fb.com James H. von Brecht California State University, Long Beach Long Beach james.vonbrecht@csulb.edu |
| 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. |