A loss framework for calibrated anomaly detection
Authors:
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | While our focus has been mostly conceptual, some illustrative experiments are presented in Appendix D. |
| Researcher Affiliation | Academia | Aditya Krishna Menon Australian National University aditya.menon@anu.edu.au Robert C. Williamson Australian National University bob.williamson@anu.edu.au |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is primarily theoretical and does not specify any particular datasets used for training or their public availability. |
| Dataset Splits | No | The paper is theoretical and does not provide specific dataset split information for validation. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values or training configurations. |