Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning Localized Spatio-Temporal Models From Streaming Data
Authors: Muhammad Osama, Dave Zachariah, Thomas Schön
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 15,696 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,830 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed method is evaluated using both synthetic and real climate data which demonstrate its ability to accurately predict data missing in spatial regions over time. |
| Researcher Affiliation | Academia | 1Uppsala University, Sweden. |
| Pseudocode | Yes | A pseudocode implementation is provided in Algorithm 1. |
| Open Source Code | Yes | Code available at github. |
| Open Datasets | Yes | Synthetic data is generated over a uniform grid and a subset of N = 700 training points are used. (Section 5.1) Here we set N = 63 503 as the number of training points. (Section 6.1) We use tropical pacific Sea Surface Temperature (SST) data (Wikle, 2011). (Section 6.1) We use precipitation data from the Climate Research Unit (CRU) time series datasets of climate variations (Jones & Harris, 2013). (Section 6.2) |
| Dataset Splits | No | The paper mentions training and testing data but does not explicitly describe a separate validation set or its split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the experiments, such as CPU/GPU models or memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | For the proposed method with Nt = 25, Ns = 15 and a spatial basis support set to L = 5 spatial units. This results in φ(s, t) being of dimension p = Ns(Nt + 1) = 390. (Section 5.1) For the proposed method we use Nt = 35, Ns = 15 and a support of L = 3 for the spatial basis, so that p = Ns(Nt + 1) = 540. (Section 5.2) |