In the modern digital era, fraud in programmatic advertising has become a multi-billion-dollar challenge, threatening the ...

Nevertheless, prior methods have predominantly centered on supervised learning, which necessitates a reliance on costly labeled datasets. In response to this challenge, graph contrastive learning ...

However, SCN is mainly used for supervised learning and its performance is limited in the case of scarce labeled data. To this end, this paper proposes semi-supervised SCN (MR-SCN) in combination with ...

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This valuable study introduces a self-supervised machine learning method to classify C. elegans postures and behaviors directly from video data, offering an alternative to the skeleton-based ...

Application of machine learning techniques to dynamically adapt replacement policy as workload and topology evolve over time will be of particular interest. The project will be conducted in a close ...

Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation ...

To address this problem, we propose a semi-supervised graph constraint dual classifier network (SSGCDCN) that can achieve efficient and stable OSC by learning unknown class features and relationships ...