Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

In practical applications, multi-view data depicting objects from assorted perspectives can
facilitate the accuracy increase of learning algorithms. However, given multi-view data, there …

In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …

Multi-view learning (MVL) has attracted increasing attention and achieved great practical
success by exploiting complementary information of multiple features or modalities …

Deep neural networks excel in supervised learning tasks but are constrained by the need for
extensive labeled data. Self-supervised learning emerges as a promising alternative …

This paper offers a comprehensive overview of machine learning (ML) methodologies and
algorithms, highlighting their practical applications in the critical domain of water resource …

In recent years, deep learning algorithms have rapidly revolutionized artificial intelligence,
particularly machine learning, enabling researchers and practitioners to extend previously …

Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …