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].
Robustness to Adversarial Perturbations in Learning from Incomplete Data
Authors: Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have also tested our method, denoted by SSDRL, via extensive computer experiments on datasets such as MNIST [16], SVHN [17], and CIFAR-10 [18]. When equipped with deep neural networks, SSDRL outperforms rivals such as Pseudo-Labeling (PL) [19] and the supervised DRL of [9] on all the datasets. |
| Researcher Affiliation | Collaboration | Amir Najafi Department of Computer Engineering Sharif University of Technology Tehran, Iran EMAIL; Shin-ichi Maeda Preferred Networks, Inc. Tokyo, Japan EMAIL; Masanori Koyama Preferred Networks, Inc. Tokyo, Japan EMAIL; Takeru Miyato Preferred Networks, Inc. Tokyo, Japan EMAIL |
| Pseudocode | Yes | Algorithm 1 Stochastic Gradient Descent for SSDRL |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | MNIST [16], SVHN [17], and CIFAR-10 [18] |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| 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 like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper states "Architecture and other specifications about our DNNs are explained in details in Appendix A." but the provided text does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings. |