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..
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. |