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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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Osama A. I. Hussain, Robert C. Moehler, Stuart D. C. Walsh and Dominic D. Ahiaga-Dagbui
Mega projects delivering rail infrastructure are constantly seeking cost-effective and efficient technologies to sustain the growing population. Building information modeling (BIM) and BIM for cost management (5D-BIM) have shown great potential in the bu...
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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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Sara El Mekkaoui, Loubna Benabbou, Stéphane Caron and Abdelaziz Berrado
Improving maritime operations planning and scheduling can play an important role in enhancing the sector?s performance and competitiveness. In this context, accurate ship speed estimation is crucial to ensure efficient maritime traffic management. This s...
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