In deterministic optimization, conjugate gradient (CG) type approaches are preferred with a
superior convergence rate than the ordinary gradient approaches. The requirement of …

Enhancing the precision of marine vessel trajectory prediction (VTP) is crucial for collision
avoidance, intelligent navigation, and crisis alert in maritime safety. Most RNN-based …

Machine reading comprehension is a crucial and challenging task in natural language
processing (NLP). Recently, knowledge graph (KG) embedding has gained massive …

Question classification is an essential task in question answering (QA) systems. An effective
and efficient question classification model can not only restrict the search space for answers …

Answer generation is one of the most important tasks in natural language processing, and
deep learning-based methods have shown their strength over traditional machine learning …

Novel hybrid neural network prediction model (denoted by E-SRWNN) is formed by
combining ensemble empirical mode decomposition (EEMD) and stochastic recurrent …

We introduce two new stochastic conjugate frameworks for a class of nonconvex and
possibly also nonsmooth optimization problems. These frameworks are built upon …

Neural Machine Translation (NMT) is an end-to-end learning approach for automated
translation, overcoming the weaknesses of conventional phrase-based translation systems …

Semantic mining is always a challenge for big biomedical text data. Ontology has been
widely proved and used to extract semantic information. However, the process of ontology …

Neural machine translation has been widely adopted in modern machine translation as it
brings the state-of-the-art performance to large-scale parallel corpora. For real-world …