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..
Adversarial Unsupervised Representation Learning for Activity Time-Series
Authors: Karan Aggarwal, Shafiq Joty, Luis Fernandez-Luque, Jaideep Srivastava834-841
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on four disorder prediction tasks using linear classifiers. Empirical evaluation demonstrates that our proposed method scales and performs better than many strong baselines. |
| Researcher Affiliation | Academia | 1University of Minnesota, 2Nanyang Technological University, 3Qatar Computing Research Institute |
| Pseudocode | No | No structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures) were found. |
| Open Source Code | No | No explicit statement about releasing source code or a link to a code repository for the described methodology was found. |
| Open Datasets | Yes | We use Study of Latinos (SOL) (Sorlie et al. 2010) and Multi-Ethnic Study of Atherosclerosis (MESA) (Bild et al. 2002) datasets. |
| Dataset Splits | Yes | We use 80%,10%,10% split for train, validation, and test sets repeated 10 times, and we report the mean scores. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running experiments were provided. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) were provided. Only general software/model types like 'Logistic Regression', 'CNN', 'LSTM', 'Adam Optimizer' are mentioned without versions. |
| Experiment Setup | Yes | The embedding size of d=100 was fixed for all the models. The weighting parameters λ and β were chosen to be 0.05 and 0.5, respectively. We tuned for w {12, 20, 30, 50, 100, 120, 500}, η {0, 0.25, 0.5, 0.75, 1}, and |N (Tk)| {2, 4} on the development set. We chose w of size 20, 20, 30, and 50 for sample2vec, hour2vec, day2vec, and week2vec, respectively. The η of 0.25 and 0.5 were chosen for day2vec and hour2vec, respectively. The neighbor set size of 2 was chosen. For the CNN baseline, 3, 4, 3, and 3-layered network were used for sleepapnea, diabetes, insomnia, and hypertension, with a dropout of 0.5 trained with Adam Optimizer. |