Before the application of deep learning techniques in NLP, the mathematical tools used were completely different to the ones adopted for speech, image, and video processing, creating a huge barrier to the flow of information between these different modes. But using deep learni...
Deep learning has presented considerable potential and is gaining more importance in computer assisted diagnosis. As the gold standard for pathologically diagnosing cervical intraepithelial lesions and invasive cervical cancer, colposcopy-guided biopsy faces challenges in improving accuracy and efficiency worldwi...
The difficulty of the traffic identification is to find the features in the flow data. The process is very time‐consuming. Also, these approaches are invalid to unknown protocol. To solve these problems, we propose a method that is based on neural network and deep learning - a hotspot of ...
deep learningenergy consumptionuser satisfactionThe diverse energy usage behaviors of various types of users within integrated energy systems have heightened the challenges of system coordination and low-carbon operation. To enhance user experience and effectively manage energy consumption, this study, based...
In this work, we explore the application of deep learning-based object detection technology in the lawn environment, providing a research example for agricultural intelligence. A dataset containing trunk, spherical tree and person is specially made for the lawn environment, which provides dataset suppor...
The application of deep learning ensures each solution to be predicted in 1 s and allows the implementation of optimization algorithms or the direct traversal of the entire parameter space. Therefore, the proposed framework can be a promising tool for multiscale modeling and analysis [50] owing ...
Application of deep learning for high-throughput phenotyping of seed: a review Seed quality is of great importance for agricultural cultivation. High-throughput phenotyping techniques can collect magnificent seed information in a rapi... C Jin,L Zhou,Y Pu,... - 《Artificial Intelligence Review》 ...
the application of deep learning models in clinical practice due to lack of interpretability, trust, and transparency. To overcome this problem, several studies on interpretability have been proposed. Therefore, in this paper, we comprehensively review the interpretability of deep learning in medical ...
depth research of the application of deep learning to fuse MR images with clinical information for predicting pCR to NAC in patients with breast cancer. This study aimed to develop a deep learning model to fuse high-dimensional MR image features and the clinical information for the pretreatment ...
"The limitations of deep learning in adversarial settings." In Security and Privacy (EuroS&P), 2016 IEEE European Symposium on, pp. 372-387. IEEE, 2016. 本文所有内容均从该论文中整理所得,遵循论文和会议的分发原则。 1.简介 众所周知,深度学习容易受到对抗样本的攻击。在文章中作者介绍了一种新的...