نتایج جستجو برای: convolutional neural network
تعداد نتایج: 836773 فیلتر نتایج به سال:
In the last few years, deep learning has lead to very good performance on a variety of problems, such as object recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Due to the lack of training data and computing power in early days, it is hard to train a large high-capaci...
The Siamese Neural Network (SNN) is a neural network architecture capable of learning similarity knowledge between cases in a case base by receiving pairs of cases and analysing the di erences between their features to map them to a multi-dimensional feature space. This paper demonstrates the development of a Convolutional Siamese Network (CSN) for the purpose of case similarity knowledge gener...
due to extraordinary large amount of information and daily sharp increasing claimant for ui benefits and because of serious constraint of financial barriers, the importance of handling fraud detection in order to discover, control and predict fraudulent claims is inevitable. we use the most appropriate data mining methodology, methods, techniques and tools to extract knowledge or insights from ...
motivated by the Convolutional Neural Networks about digit recognition and ImageNet deep neural network by Krizhevsky et al. [1], I did this project on Guqin notation recognition, which classified reduced characters with positioned 1-10 (一 -十) in handwritten Chinese characters and translated to other music recording scores. I built a four-layer convolutional neural network using adjusted CaffeN...
Introduction: Encoding models are used to predict human brain activity in response to sensory stimuli. The purpose of these models is to explain how sensory information represent in the brain. Convolutional neural networks trained by images are capable of encoding magnetic resonance imaging data of humans viewing natural images. Considering the hemodynamic response function, these networks are ...
This paper proposes a new convolutional neural architecture based on treestructures, called the tree-based convolutional neural network (TBCNN). Two variants take advantage of constituency trees and dependency trees, respectively, to model sentences. Compared with traditional “flat” convolutional neural networks (CNNs), TBCNNs explore explicitly sentences’ structural information; compared with ...
Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best predictive performance in areas such as speech and image recognition by hierarchically composing simple local features into complex models. We try to apply the conv...
Convolutional Neural Networks (CNNs) have shown to yield very strong results in several Computer Vision tasks. Their application to language has received much less attention, and it has mainly focused on static classification tasks, such as sentence classification for Sentiment Analysis or relation extraction. In this work, we study the application of CNNs to language modeling, a dynamic, seque...
نمودار تعداد نتایج جستجو در هر سال
با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید