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..
Variational Recurrent Adversarial Deep Domain Adaptation
Authors: Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, Yan Liu
ICLR 2017 | Venue PDF | 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 EMAIL |
| 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. |