Hana Ajakan

TL;DR: In this article, a new representation learning approach for domain adaptation is proposed, in which data at training and test time come from similar but different distributions, and features that cannot discriminate between the training (source) and test (target) domains are used to promote the emergence of features that are discriminative for the main learning task on the source domain.

TL;DR: A new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions, which can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer.

TL;DR: A new neural network learning algorithm suited to the context of domain adaptation, in which data at training and test time come from similar but different distributions, which has better performance than either a standard neural networks and a SVM.

TL;DR: In this article, a new representation learning approach for domain adaptation is proposed, in which data at training and test time come from similar but different distributions, and features that cannot discriminate between the training (source) and test (target) domains are used to promote the emergence of features that are discriminative for the main learning task on the source domain.

TL;DR: This paper presents Andrana, a lightweight malware detection tool for Android that leverages machine learning techniques and static analysis to determine, with an accuracy of 94.90%, if an application is malicious.