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 [1].
Graph-Guided Network for Irregularly Sampled Multivariate Time Series
Authors: Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, Marinka Zitnik
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use RAINDROP to classify time series and interpret temporal dynamics on three healthcare and human activity datasets. RAINDROP outperforms state-of-the-art methods by up to 11.4% (absolute F1-score points), including techniques that deal with irregular sampling using fixed discretization and set functions. RAINDROP shows superiority in diverse setups, including challenging leave-sensor-out settings. |
| Researcher Affiliation | Collaboration | Xiang Zhang Harvard University EMAIL Marko Zeman University of Ljubljana EMAIL Theodoros Tsiligkaridis MIT Lincoln Laboratory EMAIL Marinka Zitnik Harvard University EMAIL. M.Z. is supported, in part, by NSF under nos. IIS-2030459 and IIS-2033384, Harvard Data Science Initiative, Amazon Research Award, Bayer Early Excellence in Science Award, Astra Zeneca Research, and Roche Alliance with Distinguished Scientists Award. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and datasets are available at https://github.com/mims-harvard/Raindrop. |
| Open Datasets | Yes | For all datasets used in this work, we share downloadable links to the raw sources and processed and ready-to-run datasets with the research community through this link: https://github.com/mims-harvard/Raindrop. |
| Dataset Splits | Yes | We randomly split the dataset into training (80%), validation (10%), and test (10%) set. The indices of these splits are fixed across all methods. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions 'Python implementation' and 'Cytoscape' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In the generation of observation embedding, we set Ru as a 4-dimensional vector... The dimensions of time representation pt and rv are both 16. The trainable weight matrix D has shape of 4 32... We set the number of RAINDROP layers L as 2... We set the proportion of edge pruning as 50% (K=50)... The dk is set to 20, while the shape of W is 20 20... The da is set equal to the number of sensors. The first layer of ϕ has 128 neurons while the second layer has C neurons... We set λ = 0.02 to adjust Lr regularization scale. |