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].
Deep into Hypersphere: Robust and Unsupervised Anomaly Discovery in Dynamic Networks
Authors: Xian Teng, Muheng Yan, Ali Mert Ertugrul, Yu-Ru Lin
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both synthetic and real datasets demonstrate the consistent and robust performance of the proposed method. |
| Researcher Affiliation | Academia | 1 School of Computing and Information, University of Pittsburgh, USA 2 Graduate School of Informatics, Middle East Technical University, Turkey |
| Pseudocode | No | No pseudocode or algorithm block found in the paper. |
| Open Source Code | Yes | Code is available at https://github.com/picsolab/DeepSphere |
| Open Datasets | Yes | 1http://www.nyc.gov/html/tlc/html/about/trip record data.shtml |
| Dataset Splits | No | The paper mentions training and test data but does not explicitly provide details about a validation split or its proportion. |
| Hardware Specification | No | No specific hardware specifications (e.g., GPU/CPU models, memory details) used for experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions using “Adam Optimizer” but does not provide specific software dependencies with version numbers (e.g., Python, TensorFlow/PyTorch versions). |
| Experiment Setup | No | The paper mentions trade-off parameters like γ and λ but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations. |