recurrent random neural network
CCG Supertagging with a Recurrent Neural Network
... forward neural network to supertagging, and ad- dressed the first two problems mentioned ...conditional random field with their feed-forward tagger provided little accuracy improvement and vastly ...
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Finger vein verification algorithm based on fully convolutional neural network and conditional random field
... Table 6 expounds the performance metrics of the proposed model and the current mainstream segmentation model,where the proposed model outperforms the remaining subjects in most metrics with the accuracy at 0.8722. We ...
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Robust Exponential Stability of Periodic Solutions for Static Recurrent Neural Networks with Delays
... static recurrent neural networks by using the fixed point theory, Poineare map and Lyapunov function combined with inequality ...static recurrent neural network is a kind of ...
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Comparison of named entity recognition methodologies in biomedical documents
... Results: Our results show that, compared with a baseline having a 70.09% F1 score, the RNN Jordan‑ and Elman‑type algorithms have F1 scores of approximately 60.53% and 58.80%, respectively. When we use CRF as a machine ...
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Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks
... (threshold random walk with credit based rate limiting, rate limiting, maximum entropy and NETAD) in the ...Office) network than in the ISP (Internet Service ...
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On predicting the outcomes of chemotherapy treatments in Breast cancer
... Abstract. Chemotherapy is the main treatment commonly used for treating cancer patients. However, chemotherapy usually causes side ef- fects some of which can be severe. The effects depend on a variety of factors ...
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Unsupervised Recurrent Neural Network Grammars
... dict Network (PRPN) (Shen et ...ral network (and among the current state-of-the-art in grammar induction from words on the full cor- pus); (3) RNNG with trivial trees (left branching, right branching, ...
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Random neural network learning heuristics
... for recurrent RNN in Gelenbe [30] which can be applied to a feed forward RNN ...this recurrent network was also presented in Gelenbe and Timotheou ...class random neural network ...
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Evaluating Recurrent Neural Network Explanations
... Perturbation Experiment. In order to evalu- ate the selectivity of word relevances, we perform a perturbation experiment aka “pixel-flipping“ in computer vision (Bach et al., 2015; Samek et al., 2017), i.e. we remove ...
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Uncertainty in Recurrent Neural Network with Dropout
... 2 random variable, and a sample from this random variable is a dish we ...A random variable (under a distribution) has ...as random variables of a ...
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Recurrent Neural Network based Rule Sequence Model for Statistical Machine Translation
... novel recurrent neural network based rule sequence model (RNN-RSM), which utilizes the representational power of recurrent neural network (RNN) to capture arbitrary distance of ...
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Modelling and trading the English stock market with novelty optimization techniques
... order neural network (HONN), a recurrent neural network (RNN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD), plus a buy and hold ...
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Novel Ensemble Neural Network Models for better Prediction using Variable Input Approach
... Artificial Neural Network (ANN) models, namely, Multilayer Perceptron Network (MLPN), Elman Recurrent Neural Network (ERNN), Radial Basis Function Network (RBFN), Hopfield ...
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Enhancing recurrent neural network-based language models by word tokenization
... the network are the previous n-words according to the language models ...final network output is computed using the Softmax activation function [3] to ensure that network output is a valid ...
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Leveraging text skeleton for de-identification of electronic medical records
... In this challenge, eight PHI categories, Patients, Doctors, Hospitals, IDs, Dates, Locations, Phone numbers and Ages, were used to annotate Partner Healthcare data. The competing systems employed rule-based [8] and ...
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A NEAT Approach to Malware Classification
... of neural networks, The fitter parent can be more likely to pass on its node and connection layout to its children, while the less fit parent has a smaller chance, but a chance ...
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Detecting hate speech on Twitter using a convolution-GRU based deep neural network
... Comparison against similar DNNs. Our network architecture is similar to those in [5,19,22]. The differences include: 1) we use a GRU instead of an LSTM for the reasons stated before; 2) we add a drop-out layer to ...
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Solar Irradiance Forecasting using Deep Neural Networks
... artificial neural networks (ANN), support vector machines (SVM), or autoregressive moving average ...deep neural networks. Deep recurrent neural networks (DRNNs) add complexity to the model ...
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Hybrid Neural Networks for Learning the Trend in Time Series
... of recurrent neural networks with sophisticated recurrent hidden and gated units, are particularly successful and popular due to its ability to learn hidden long-term sequential ...
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3D Firework Reconstruction from a Given Videos
... different neural networks including 3D Convolution Neural Network (3D-CNN) and Recurrent Neural Network(RNN) are designed respectively to extract these parameters needed by our ...
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