[PDF] Top 20 Understanding Convolutional Neural Networks for Text Classification
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Understanding Convolutional Neural Networks for Text Classification
... the word-level, but instead form slot activation patterns that give different types of ngrams similar activation strengths. This provides empirical evi- dence that filters are not homogeneous. By clus- tering ... See full document
10
Classification of lung sounds using convolutional neural networks
... The convolutional network architecture is a remarkably versatile yet conceptually simple paradigm that can be ap- plied to a wide spectrum of perceptual ...tional networks are trainable, multistage ...[11]. ... See full document
9
Atrial fibrillation classification based on convolutional neural networks
... 30 convolutional neural net- works (i.e., 6 Alex networks and 24 residual networks) for the diagnosis of the NSR ...5 convolutional layers, 3 fully connected layers and the number of ... See full document
6
Hierarchical Classification with Convolutional Neural Networks for Biomedical Literature
... Considering the numerous classes of the documents and the unbalanced distributions of samples, we introduce a hierarchical CNN-based framework in order to solve multi-class and multi-label semantic indexing with ... See full document
9
Combining Recurrent and Convolutional Neural Networks for Relation Classification
... Due to the repetition of the middle context, we force the network to pay special attention to it. The two contexts are processed by two independent convo- lutional and max-pooling layers. After pooling, the results are ... See full document
6
Inter-Class Angular Loss for Convolutional Neural Networks
... a convolutional feature learning machine supervised by the softmax ...The convolutional layers extract the discriminative features of an input image, and send the features into the softmax layer for ... See full document
8
Clinical text classification with rule-based features and knowledge-guided convolutional neural networks
... clinical text classification rely on biomedical knowledge sources ...narrative text to concepts from knowledge sources like Unified Medical Language System (UMLS), then train classifiers on document ... See full document
9
Convolutional Neural Networks for Financial Text Regression
... artificial neural net- work (ANN) solution which does not use a lexicon or any other manually labeled ...lutional neural network (CNN) model is designed similar to Bitvai and Cohn (2015) and Kim ...long ... See full document
7
Short Text Clustering via Convolutional Neural Networks
... One way is to expand and enrich the context of da- ta. For example, Banerjee et al. (2007) proposed a method of improving the accuracy of short tex- t clustering by enriching their representation with additional features ... See full document
8
An Automated System for Identification of Skeletal Maturity using Convolutional Neural Networks Based Mechanism
... 4 convolutional layers where the layers have 5X5 sized 32 filters, 3X3 sized 64 filters, 3X3 sized 64 filters, 3X3 sized 32 filters ...radius classification results. We can observe that the ... See full document
7
Cluster Gated Convolutional Neural Network for Short Text Classification
... or neural networks (Wang et ...short text classification, but the performance of such methods is strongly dependent on the quality of knowledge bases and constructing a large-scale knowledge ... See full document
10
A Novel Method for Remotely Sensed Hyperspectral Image Classification Based on Convolutional Neural Network
... image classification is the process of assigning land cover classes to ...and classification approaches affect the success of ...classification. Convolutional Neural Networks ... See full document
10
A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification
... rameter. We take inspiration from previous empir- ical analyses of neural models due to Coates et al. (2011) and Breuel (2015), which investigated fac- tors in unsupervised feature learning and hyperpa- rameter ... See full document
11
Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images
... of convolutional neural networks and reduce the sensitivity to network initialization, a Batch Normalization layer is used between convolutional layers and nonlinearities, such as ReLU ... See full document
13
Deep machine learning provides state of the art performance in image based plant phenotyping
... performance. Neural networks re- quire non-linear functions between layers in order to capture the complex non-linearity of the classification ...each convolutional filter at each position is ... See full document
10
Transparent text quality assessment with convolutional neural networks
... recurrent neural networks at the word ...hierarchical neural network that encodes word sequences to sentence representations, and sentence representations to essay representations, in both cases ... See full document
5
Deep Pyramid Convolutional Neural Networks for Text Categorization
... nal data size (as well as per-layer computation) shrinks in a pyramid shape. The network depth can be treated as a meta-parameter. The computa- tional complexity of this network is bounded to be no more than twice that ... See full document
9
Very Deep Convolutional Networks for Text Classification
... Most of the previous applications of ConvNets to NLP use an architecture which is rather shal- low (up to 6 convolutional layers) and combines convolutions of different sizes, e.g. spanning 3, 5 and 7 tokens. This ... See full document
10
Brain Tumor Classification Using Convolutional Neural Networks
... normal neural network, image cannot scalable. But in convolution neural network, image can scalable ...Convolution Neural Network (CNN) consists of input layer, convolution layer, Rectified Linear ... See full document
5
Sequential Short Text Classification with Recurrent and Convolutional Neural Networks
... short- text representation level and the class representa- tion level does not help in most cases and may even lower the ...short- text representations contain richer and more gen- eral information than ... See full document
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