2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets.
Xiaoxi WeiA. Aldo FaisalMoritz Grosse-WentrupAlexandre GramfortSylvain ChevallierVinay JayaramCamille JeunetStylianos BakasSiegfried LudwigKonstantinos BarmpasMehdi BahriYannis PanagakisNikolaos A. LaskarisDimitrios A. AdamosStefanos ZafeiriouWilliam C. DuongStephen M. GordonVernon J. LawhernMaciej SliwowskiVincent RouannePiotr TempczykPublished in: NeurIPS (Competition and Demos) (2021)
Keyphrases
- transfer learning
- data sets
- learning tasks
- knowledge transfer
- labeled data
- data sources
- semi supervised learning
- active learning
- reinforcement learning
- cross domain
- text categorization
- machine learning
- text classification
- multi task learning
- unlabeled data
- manifold alignment
- structure learning
- multi task
- domain adaptation
- cross domain learning
- training set
- training data
- learning algorithm
- machine learning algorithms
- data streams
- e learning
- databases
- high dimensional data
- knowledge management
- collaborative filtering
- semi supervised
- pairwise
- conditional independence
- multiple tasks
- previously learned
- transferring knowledge