Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalization
Authors: Chengtao Jian, Kai Yang, Yang Jiao
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world datasets have been conducted to elucidate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Chengtao Jian Tongji University, Shanghai, China jct@tongji.edu.cn Kai Yang Tongji University, Shanghai, China kaiyang@tongji.edu.cn Yang Jiao Tongji University, Shanghai, China yangjiao@tongji.edu.cn |
| Pseudocode | Yes | Algorithm 1 SLA: Stratified Localization Algorithm |
| Open Source Code | No | While the data used in our study is publicly available, we are currently unable to provide open access to the code. |
| Open Datasets | Yes | HHAR [Blunck et al., 2015], PAMAP [Reiss, 2012], WESAD [Philip Schmidt et al., 2018], SWELL [Koldijk et al., 2014], USC-HAD[Zhang and Sawchuk, 2012] and DSADS [Barshan and Altun, 2013]. |
| Dataset Splits | No | The paper mentions 'training dataset Dtrain' and 'test dataset Dtest' but does not explicitly specify a validation set or the percentages for all three splits. |
| Hardware Specification | Yes | All the methods are implemented with Py Torch[Paszke et al., 2019] version 1.7.1 on an NVIDIA Ge Force RTX 4090 graphics card. |
| Software Dependencies | Yes | All the methods are implemented with Py Torch[Paszke et al., 2019] version 1.7.1 |
| Experiment Setup | Yes | Our baseline experiments were conducted using a network architecture consisting of 10-layers dilated convolutions network. The dilation rate for each layer is set to 2k, where k is the layer number. We used the same kernel size of 3 across all layers. Optimization was performed using the Adam optimizer with a weight decay of 3 10 4. For all baseline experiments, we set the batch size to 256 and the learning rate to 0.002. The training was set to run for a maximum of 50 epochs. |