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Andreas Nugaard Holm, Dustin Wright and Isabelle Augenstein
Uncertainty approximation in text classification is an important area with applications in domain adaptation and interpretability. One of the most widely used uncertainty approximation methods is Monte Carlo (MC) dropout, which is computationally expensi...
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Xuan Di, Rongye Shi, Zhaobin Mo and Yongjie Fu
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DN...
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L. Thanga Mariappan, J. Arun Pandian, V. Dhilip Kumar, Oana Geman, Iuliana Chiuchisan and Carmen Nastase
Cryptocurrency has emerged as a well-known significant component with both economic and financial potential in recent years. Unfortunately, Bitcoin acquisition is not simple, due to uneven business and significant rate fluctuations. Traditional approache...
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Hee Min Teh, Faris Ali Hamood Al-Towayti, Vengatesan Venugopal and Zhe Ma
This experimental study investigated the hydrodynamic performance of the first free-surface semicircular breakwater supported on piles under regular waves. The research focused on SCB models with porosity levels of 0%, 9%, 18%, and 27%. Experimental test...
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Chen Chen, Weidong Zhou and Lina Gao
A suitable jump Markov system (JMS) filtering approach provides an efficient technique for tracking surface targets. In complex surface target tracking situations, due to the joint influences of lost measurements with an unknown probability and heavy-tai...
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