recurrent neural network training
Recurrent Neural Network Training using ABC Algorithm For Traffic Volume Prediction
... higher network complexity which suggests the need for Deep Neural ...the network has to be split into three sets; training set, validation set and the testing ...the training, ...
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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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Unsupervised Recurrent Neural Network Grammars
... Recurrent neural network grammars (RNNGs) (Dyer et ...for training. In this work, we explore unsupervised learning of recurrent neural network grammars for language ...
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Decoder Integration and Expected BLEU Training for Recurrent Neural Network Language Models
... with recurrent networks that maintained the usual n-gram context but kept a beam of hidden layer configurations at each state (Auli et ...similar recurrent his- tories that can be safely ...
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Image Description Using Deep Neural Network
... deep neural networks and recurrent neural networks ...Long-Term Recurrent Convolutional Network ...the training time required for the Neural Image Captioning (NIC) ...
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Video Classification with Recurrent Neural Network
... RMLP neural network for classification. The RMLP neural network classify each video with its category by calculating error term and weighted sum and generate class for ...of neural ...
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CCG Supertagging with a Recurrent Neural Network
... Datasets and Baseline. We follow the standard splits of CCGBank (Hockenmaier and Steedman, 2007) for all experiments using sections 2-21 for training, section 00 for development and section 23 as in-domain test ...
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Incremental Recurrent Neural Network Dependency Parser with Search based Discriminative Training
... current neural network (RNN) that pre- dicts the actions for a fast and accurate shift-reduce dependency ...ing training, giving us both faster training and a form of backoff ...
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Recurrent Neural Network Grammars
... in neural parsing by Hender- son (2004), who hypothesized that larger, unstruc- tured conditioning contexts are harder to learn from, and provide opportunities to ...larger training sets obtained through ...
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Evaluating Recurrent Neural Network Explanations
... ral network model and a single test data point 1 ...as training data statistics, sampling, or are optimization-based (Ribeiro et ...no recurrent neural network explanation method was ...
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Enhancing recurrent neural network-based language models by word tokenization
... modified recurrent neural network-based language model for language ...the network input into three ...basic recurrent neural network ...smaller training times and ...
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Recurrent Neural Network based Rule Sequence Model for Statistical Machine Translation
... first one is breaking the rules into bilingual word- pairs and use a n-gram translation model to incorpo- rate lexical dependencies that span rule boundaries (Marino et al., 2006; Durrani et al., 2013). These n- gram ...
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Complex Valued Recurrent Neural Network: From Architecture to Training
... Recurrent Neural Networks were invented a long time ago, and dozens of different architectures have been ...generalize recurrent architectures to a state space model, and we also generalize the ...
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Identification and Adaptive Control of Dynamic Nonlinear Systems Using Sigmoid Diagonal Recurrent Neural Network
... the neural network output ...proposed network, there is no need to use momentum term or learning rate adaptation [8, 10] because the training is done using the adaptation of the sigmoid ...
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Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules
... Here we have presented our deep-learning model for chemical named entities recognition in biomedical texts, trained and evaluated on the CHEMDNER corpus. Given its high performance, the model proves that chemical named ...
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Robust Exponential Stability of Periodic Solutions for Static Recurrent Neural Networks with Delays
... artificial neural network have attracted the great interest of scientists and become one of the hotspots in the field of nonlinear ...artificial neural network is a nonlinear information ...
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FACE DETECTION WITH SKIN COLOR AND FEATURES AND RECOGNIZATION USING GENETIC ALGORITHM
... and training neural network models from scratch can be high, another feature employed in this work was to guarantee that when a new offspring is generated it does not duplicate any chromosome ...
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Deep Auto-Encoder Neural Network for Phishing Website Classification
... Al Momani et al. presented a new idea that showed exceptional results in terms of true positive, true negative, sensitivity, accuracy, F-measure and general correctness compared with other methods. Additionally, the ...
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Modelling and trading the English stock market with novelty optimization techniques
... adaptive hybrid approach to utilizing two algorithms. Furthermore, this investigation also fills a gap in current financial forecasting and trading literature by imposing input selection criteria as a pre-selection ...
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Application of Artificial Intelligence for Epilepsy Disease
... Deep learning is a precise structure of the group of machine learning methods. Deep learning is a precise structure of depiction- based learning, where a system assimilates and forms fundamental aspects from each ...
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