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.
Contrastive Learning for Unsupervised Domain Adaptation of Time Series
Authors: Yilmazcan Ozyurt, Stefan Feuerriegel, Ce Zhang
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 54,543 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,604 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 | We evaluate our framework using a wide range of time series datasets to demonstrate its effectiveness and show that it achieves state-of-the-art performance for time series UDA. |
| Researcher Affiliation | Academia | Yilmazcan Ozyurt ETH Zürich EMAIL Stefan Feuerriegel LMU Munich EMAIL Ce Zhang ETH Zürich EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. The framework is described narratively and visually (Figure 1). |
| Open Source Code | Yes | 1Codes are available at https://github.com/oezyurty/CLUDA . |
| Open Datasets | Yes | We conduct extensive experiments using established benchmark datasets, namely WISDM (Kwapisz et al., 2011), HAR (Anguita et al., 2013), and HHAR (Stisen et al., 2015). |
| Dataset Splits | Yes | We split the patients of each dataset into 3 parts for training/validation/testing (ratio: 70/15/15). |
| Hardware Specification | Yes | For training and testing, we used NVIDIA Ge Force GTX 1080 Ti with 11GB GPU memory. |
| Software Dependencies | No | The paper mentions "Py Torch" as the implementation framework but does not specify a version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | In this section, we provide details on the hyperparameters tuning. Table 7 lists the tuning range of all hyperparameters. |