Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalization
Authors: Chengtao Jian, Kai Yang, Yang Jiao
NeurIPS 2024 | Venue PDF | 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 EMAIL Kai Yang Tongji University, Shanghai, China EMAIL Yang Jiao Tongji University, Shanghai, China EMAIL |
| 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. |