Continuous Temporal Domain Generalization
Authors: Zekun CAI, Guangji Bai, Renhe Jiang, Xuan Song, Liang Zhao
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness and efficiency of our approach. The code can be found at: https://github.com/Zekun-Cai/Koodos. In this section, we present the performance of the Koodos in comparison to other approaches through both quantitative and qualitative analyses. |
| Researcher Affiliation | Academia | Zekun Cai1,4, Guangji Bai2, Renhe Jiang1*, Xuan Song3,4, and Liang Zhao2 1The University of Tokyo, Tokyo, Japan 2Emory University, Atlanta, GA, USA 3Jilin University, Changchun, China 4Southern University of Science and Technology, Shenzhen, China |
| Pseudocode | No | The paper does not contain a clearly labeled "Pseudocode" or "Algorithm" block. It describes the framework and optimization using text and mathematical equations. |
| Open Source Code | Yes | The code can be found at: https://github.com/Zekun-Cai/Koodos. |
| Open Datasets | Yes | We compare with classification datasets: Rotated Moons (2-Moons), Rotated MNIST (Rot-MNIST), Twitter Influenza Risk (Twitter), and Yearbook; and the regression datasets: Tropical Cyclone Intensity (Cyclone), House Prices (House). More details can be found in Appendix A.1.1. Rotated MNIST This dataset is a variant of the classic MNIST dataset [12]. Yearbook The Yearbook dataset [54]. Cyclone The Cyclone dataset [10]. |
| Dataset Splits | No | All models were trained on training domains and then deployed on all unseen test domains. We use the last 30% domain of each dataset as test domains, which are marked with gray shading. |
| Hardware Specification | Yes | All experiments are conducted on a 64-bit machine with two 20-core Intel Xeon Silver 4210R CPU @ 2.40GHz, 378GB memory, and four NVIDIA Ge Force RTX 3090. |
| Software Dependencies | No | We use Adam Optimizer for all experiments, and we specify the architecture as well as other details for each dataset as follows: We use Neural ODEs [11] as the ODEs solvers. |
| Experiment Setup | Yes | Detailed experiment settings (i.e., dataset details, baseline details, hyperparameter settings, ablation study, scalability analysis, and sensitivity analysis) are demonstrated in Appendix A.1. The Predictive Model consists of 3 hidden layers, with a dimension of 50 each. We use a Re LU layer after each layer and a Sigmoid layer after the output layer. ... The learning rate for the Predictive Model is set at 1 10 2, while for the others is set at 1 10 3. |