Adversarial Unsupervised Representation Learning for Activity Time-Series
Authors: Karan Aggarwal, Shafiq Joty, Luis Fernandez-Luque, Jaideep Srivastava834-841
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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. |