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..
Interpolation-Prediction Networks for Irregularly Sampled Time Series
Authors: Satya Narayan Shukla, Benjamin Marlin
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We investigate the performance of this architecture on both classification and regression tasks, showing that our approach outperforms a range of baseline and recently proposed models. |
| Researcher Affiliation | Academia | Satya Narayan Shukla College of Information and Computer Sciences University of Massachusetts Amherst EMAIL Benjamin M. Marlin College of Information and Computer Sciences University of Massachusetts Amherst EMAIL |
| Pseudocode | No | The paper contains mathematical equations and architectural diagrams but no explicit 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | 1Our implementation is available at : https://github.com/mlds-lab/interp-net |
| Open Datasets | Yes | We test the model framework on two publicly available real-world datasets: MIMIC-III 3 a multivariate time series dataset consisting of sparse and irregularly sampled physiological signals collected at Beth Israel Deaconess Medical Center from 2001 to 2012 (Johnson et al., 2016), and UWave Gesture 4 a univariate time series data set consisting of simple gesture patterns divided into eight categories (Liu et al., 2009). |
| Dataset Splits | Yes | For MIMIC-III, we create our own dataset (appendix A.1) and report the results of a 5-fold cross validation experiment... and Out of the training data, 30% is used for validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We use cross-entropy loss for classification and squared error for regression. We also include ℓ2 regularizers for both the interpolation and prediction networks parameters. δI, δP , and δR are hyper-parameters that control the trade-off between the components of the objective function. |