Interpretable Sequence Learning for Covid-19 Forecasting
Authors: Sercan Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long Le, Vikas Menon, Shashank Singh, Leyou Zhang, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that our model provides more accurate forecasts compared to the alternatives, and that it provides qualitatively meaningful explanatory insights. ... 6 Experiments We conduct all experiments on US COVID-19 data. |
| Researcher Affiliation | Industry | Sercan O. Arık, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. Le, Vikas Menon, Shashank Singh, Leyou Zhang, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister Google Cloud AI {soarik,chunliang,jinsungyoon,sinharaj,aepshtey,longtle,vikasmenon, shashanksi,leyouz,mnikoltchev,yashks,hootan,ekanal,tpfister}@google.com |
| Pseudocode | Yes | Algorithm 1 Pseudo-code for training the proposed model |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for their method or a link to a code repository. |
| Open Datasets | Yes | We conduct all experiments on US COVID-19 data. The primary ground truth data for the progression of the disease, for Q and D, are from [39] as used by several others, e.g. [28]. They obtain the raw data from the state and county health departments. ... Ground truth data for the H, C and V (see Fig. 1) are obtained from [40]. |
| Dataset Splits | Yes | We split the observed data into training and validation with the last τ timesteps to mimic the testing scenario. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like TensorFlow and XGBoost, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We choose the compartment weights λD = λQ = 0.1, λH = 0.01 and λR(d) = λC = λV = 0.001. We employ [41] for hyperparameter tuning (including all the loss coefficients, learning rate, and initial conditions) with the objective of optimizing for the best validation loss, with 400 trials and we use F = 300 fine-tuning iterations. ... In the first stage of training, we use teacher forcing with ν [0, 1], which is a hyperparameter. For fine-tuning (please see below), we use ν = 1 to unroll the last τ steps to mimic the real forecasting scenario. |