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..
Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation
Authors: Qiao Liu, Hui Xue
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results of extensive experiments on several real-world UTSDA tasks verify the effectiveness of our proposed method. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China 2MOE Key Laboratory of Computer Network and Information Integration (Southeast University), China |
| Pseudocode | Yes | Algorithm 1 Adv SKM |
| Open Source Code | Yes | Our codes 1 are published online. 1https://github.com/jarheadjoe/Adv-spec-ker-matching |
| Open Datasets | Yes | The first dataset is the Human Activity Recognition (HAR) dataset [Anguita et al., 2013] which contains accelerometer, gyroscope, and estimated body acceleration data from 30 participants. The second is the Heterogeneity Human Activity Recognition (HHAR) dataset [Stisen et al., 2015]... Next is WISDM activity recognition (WS AR) dataset [Kwapisz et al., 2011]... Finally, hand gesture accelerometer data from 8 participants is provided from the gesture recognition dataset (u Wave) [Liu et al., 2009]. |
| Dataset Splits | Yes | We split the data in each dataset into training, validation and test. The training-test were split at 80% and 20%, respectively, and the training data were further split into training-validation with the same proportions. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions implementing the framework and using the ADAM algorithm, but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | The framework was trained for 30,000 iterations, which means that the best model was selected from nine models. The influence of parameter variations on accuracy is shown in Fig. 5(a)-5(b). Hyperparameters λ, L and M. |