Variational Recurrent Adversarial Deep Domain Adaptation
Authors: Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, Yan Liu
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments on real-world multivariate healthcare time-series datasets, we empirically demonstrate that learning temporal dependencies helps our model s ability to create domain-invariant representations, allowing our model to outperform current state-of-the-art deep domain adaptation approaches. |
| Researcher Affiliation | Academia | Sanjay Purushotham*, Wilka Carvalho*, Tanachat Nilanon, Yan Liu Department of Computer Science University of Southern California Los Angeles, CA 90089, USA {spurusho,wcarvalh,nilanon,yanliu.cs}@usc.edu |
| Pseudocode | No | The paper provides mathematical formulations and block diagrams of the model but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | Footnote [3] on page 4 states: 'Codes will be publicly released soon', indicating future availability rather than current. |
| Open Datasets | Yes | MIMIC-III( Johnson et al. (2016)) is a public dataset with deidentified clinical care data collected at Beth Israel Deaconess Medical Center from 2001 to 2012. ... Child-AHRF dataset: This is a PICU dataset which contains health records of 398 children patient with acute hypoxemic respiratory failure in the intensive care unit at Children s Hospital Los Angeles (CHLA)(Khemani et al. (2009)). |
| Dataset Splits | Yes | Source domain data was split into train/validation subsets with a 70/30 ratio and target domain data into train/validation/test subsets with a 70/15/15 ratio. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer ( Kingma & Ba (2014))' but does not provide specific version numbers for other key software components, libraries, or programming languages. |
| Experiment Setup | Yes | All the deep domain adaptation models including ours had depth of size 8 (including output classifier layers). We used the Adam optimizer ( Kingma & Ba (2014)) and ran all models for 500 epochs with a learning rate of 3e 4. We set an early stopping criteria that the model does not experience a decrease in the validation loss for 20 epochs. |