… such as machine learning and non-deep neural networks are … In this study, a hybrid deep
learning neural network … that using the proposed hybrid deep learning neural network for …

… memory (LSTM) deep learning neural network to simulate … neural network and three deep
learning auxiliary algorithms in the … using multiscale deep feature learning with hybrid models. …

… Deep learning can be integrated into the recommendation method to … on deep learning,
and finally summarizes and prospects the recommendation methods based on deep learning. …

摘要 This paper introduces a new method to extract speaker embed-dings from a deep neural
network (DNN) for text-independent speaker verification. Usually, speaker embeddings …

… low accuracy and traditional deep learning network could not … -BiLSTM model and traditional
deep learning model, the … guided ammunitions, a hybrid piezoelectric stator with sandwich …

… In recent years, deep learning technology has greatly … and recent methods based on deep
learning. Finally, the potential … feature extraction, computer vision, deep learning Citation …

… These problems in resource aspect partially lead to the limited performance of deep
learning modeling. However, this task can be viewed as a low resource NLP task to challenge. …

… -resolution reconstruction method based on deep learning are analyzed and compared.
Finally, the future development trend of image superresolution reconstruction is prospected. …

… traffic flow prediction model based on the hybrid deep learning (HDL). The proposed method
… were used to establish the framework of the deep learning model. The lane-level speeds of …

… power integrated into the power system, a hybrid deep learning model-based approach
for wind power … Secondly, a hybrid deep learning model of convolutional neural network (CNN), …