Multi-Time Attention Networks for Irregularly Sampled Time Series
Authors: Satya Narayan Shukla, Benjamin Marlin
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | 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 {snshukla,marlin}@cs.umass.edu |
| 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. |