This work addresses the challenging domain adaptation setting in which
k...
Obtaining sufficient labelled data for model training is impractical for...
The scarcity of labeled data is one of the main challenges of applying d...
Unsupervised Domain Adaptation (UDA) has emerged as a powerful solution ...
Learning time-series representations when only unlabeled data or few lab...
Unsupervised domain adaptation methods aim to generalize well on unlabel...
Re-ranking models refine the item recommendation list generated by the p...
Unsupervised domain adaptation (UDA) has successfully addressed the doma...
Sleep staging is of great importance in the diagnosis and treatment of s...
Learning decent representations from unlabeled time-series data with tem...
Latent factor models play a dominant role among recommendation technique...
Graph-based recommendation models work well for top-N recommender system...
Accurate estimation of remaining useful life (RUL) of industrial equipme...
Disease-gene association through Genome-wide association study (GWAS) is...
Real-world networks often exist with multiple views, where each view
des...
This paper focuses on scalability and robustness of spectral clustering ...
Ensemble clustering has been a popular research topic in data mining and...
The emergence of high-dimensional data in various areas has brought new
...