Event Outlier Detection in Continuous Time
Authors: Siqi Liu, Milos Hauskrecht
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To test the performance of the methods, we conduct experiments on both synthetic data and real-world clinical data and show the effectiveness of the proposed methods. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA 2Borealis AI, Vancouver, BC, Canada. |
| Pseudocode | No | The paper describes the methods using mathematical formulations and text, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation of all the methods and the experiments is publicly available.1 https://github.com/siqil/CPPOD |
| Open Datasets | Yes | In this part, we use real-world clinical data extracted from the MIMIC III dataset (Johnson et al., 2016). |
| Dataset Splits | Yes | We choose the number of hidden units in the model from {64, 128, 256, 512, 1024} by maximizing the likelihood on the internal validation set that consists of 20 percent of the training set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU/CPU models or other system specifications. |
| Software Dependencies | No | The paper mentions using a 'continuous-time LSTM' model, but it does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We choose the number of hidden units in the model from {64, 128, 256, 512, 1024} by maximizing the likelihood on the internal validation set that consists of 20 percent of the training set. |