Multi-output Polynomial Networks and Factorization Machines
Authors: Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental results |
| Researcher Affiliation | Collaboration | Mathieu Blondel NTT Communication Science Laboratories Kyoto, Japan mathieu@mblondel.org Vlad Niculae Cornell University Ithaca, NY vlad@cs.cornell.edu Takuma Otsuka NTT Communication Science Laboratories Kyoto, Japan otsuka.takuma@lab.ntt.co.jp Naonori Ueda NTT Communication Science Laboratories RIKEN Kyoto, Japan ueda.naonori@lab.ntt.co.jp |
| Pseudocode | Yes | Algorithm 1 Multi-output PN/FM training |
| Open Source Code | No | The paper does not contain an explicit statement that the authors are releasing their code or a direct link to a source code repository for the methodology described. |
| Open Datasets | Yes | For our multi-class experiments, we use four publicly-available datasets: segment (7 classes), vowel (11 classes), satimage (6 classes) and letter (26 classes) [12]. For our recommendation system experiments, we use the Movie Lens 100k and 1M datasets [14]. |
| Dataset Splits | Yes | Throughout our experiments, we use 50% of the data for training, 25% for validation, and 25% for evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions using a multi-class logistic loss and that hyperparameters were chosen to maximize validation accuracy, but it does not provide concrete hyperparameter values, training configurations, or system-level settings. |