On the Calibration of Multiclass Classification with Rejection
Authors: Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct experiments to validate the relevance of our theoretical findings. In this section, we report the results of two experiments based on synthetic and benchmark datasets. |
| Researcher Affiliation | Academia | 1 The University of Tokyo, Japan 2 RIKEN Center for Advanced Intelligence Project, Japan |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code or a link to a code repository. |
| Open Datasets | Yes | M. Lichman et al. UCI machine learning repository, 2013. URL http://archive.ics. uci.edu/ml. |
| Dataset Splits | No | The paper mentions 'training data' and 'test data' but does not specify explicit train/validation/test splits, percentages, or absolute sample counts for each split. It mentions 'training data size is 10,000 per class' without defining the split methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions 'AMSGRAD [21] was used for optimization' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | For all methods, we used one-hidden-layer neural networks with the rectified linear units (Re LU) as activation functions, where the number of hidden units is 3 for synthetic datasets, and 50 for benchmark datasets. We added weight decay with candidates {10 7, 10 4, 10 1}. |