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..
Multi-Time Attention Networks for Irregularly Sampled Time Series
Authors: Satya Narayan Shukla, Benjamin Marlin
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate the performance of this framework on interpolation and classification tasks using multiple datasets. Our results show that the proposed approach performs as well or better than a range of baseline and recently proposed models while offering significantly faster training times than current state-of-the-art methods. |
| Researcher Affiliation | Academia | Satya Narayan Shukla & Benjamin M. Marlin College of Information and Computer Sciences University of Massachusetts Amherst Amherst, MA 01003, USA EMAIL |
| Pseudocode | No | The paper provides architectural diagrams and mathematical equations for its model but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Implementation available at : https://github.com/reml-lab/m TAN |
| Open Datasets | Yes | All the datasets used in the experiments are publicly available and can be downloaded using the following links: Physio Net: https://physionet.org/content/challenge-2012/ MIMIC-III: https://mimic.physionet.org/ Human Activity: https://archive.ics.uci.edu/ml/datasets/Localization+ Data+for+Person+Activity. |
| Dataset Splits | Yes | We randomly divide the data set into a training set containing 80% of the instances, and a test set containing the remaining 20% of instances. We use 20% of the training data for validation. |
| Hardware Specification | Yes | All experiments were run on a Nvidia Titan X GPU. |
| Software Dependencies | No | The paper mentions the use of Adam Optimizer and GRU models but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For classification, experiments are run for 300 iteration with learning rate 0.0001, while for interpolation task experiments are run for 500 iterations with learning rate 0.001. |