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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Transfer Anomaly Detection by Inferring Latent Domain Representations
Authors: Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the proposed method is demonstrated through experiments using one synthetic and four real-world datasets. |
| Researcher Affiliation | Industry | Atsutoshi Kumagai NTT Software Innovation Center NTT Secure Platform Laboratories EMAIL Tomoharu Iwata NTT Communication Science Laboratories EMAIL Yasuhiro Fujiwara NTT Communication Science Laboratories EMAIL |
| Pseudocode | No | The paper describes the proposed method and equations but does not include a formal pseudocode or algorithm block. |
| Open Source Code | No | The proposed method was implemented by Chainer [45]. |
| Open Datasets | Yes | We used four real-world public datasets: MNIST-r, Anuran Calls, Landmine, and Io T. ... Due to the length limit of the paper, the details of the datasets including download links are provided in the supplemental material. |
| Dataset Splits | No | We selected hyperparameters using average validation AUC on the source domains for all methods except for AE. |
| Hardware Specification | Yes | We used the following setup: CPU was Intel Xeon E5-2660v3 2.6 GHz, the memory size was 128 GB, and GPU was NVIDIA Tesla k80. |
| Software Dependencies | No | The proposed method was implemented by Chainer [45]. |
| Experiment Setup | Yes | We set the hyperparameters as follows: the regularization parameter of the AUC loss λ was 104, the dimension of the latent domain vector K was 20, the regularization parameter of the latent domain vector β was one, and the sample size of the reparametrization trick L was one. |