training recurrent neural network
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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Unsupervised Recurrent Neural Network Grammars
... modeling of syntax helps generalization even with richly-parameterized neural models. Encouraged by these observations, we also experiment with a hybrid approach where we train a supervised RNNG first and continue ...
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CCG Supertagging with a Recurrent Neural Network
... the training data; for out-of-vocabulary words, three separate randomly initialized embeddings are used for lower-case alphanumeric words, upper-case alphanumeric words, and non-alphanumeric ...
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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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Decoder Integration and Expected BLEU Training for Recurrent Neural Network Language Models
... word and phrase-penalties, a hierarchical reorder- ing model (Galley and Manning, 2008), a linear distortion feature, and a modified Kneser-Ney lan- guage model trained on the target-side of the paral- lel data. ...
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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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Incremental Recurrent Neural Network Dependency Parser with Search based Discriminative Training
... previous neural net- work models that we use and extend in this paper is the decomposition of input feature parameters using vector-matrix multiplication (Bengio et ...
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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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Hybrid Data Model Parallel Training for Sequence to Sequence Recurrent Neural Network Machine Translation
... Table 3 summarizes the main results of our experiment. In Table 3, “SRC tokens / sec” indicates the number of source tokens processed in one second. This is a standard measure for evaluating training speed; it is ...
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A Hybrid Recurrent Neural Network For Music Transcription
... the training set. For training the stacked RNN models, the training tracks were further divided into sub-sequences of length 200 and the models were trained by Back-Propagation Through Time (BPTT) ...
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A Hybrid Recurrent Neural Network For Music Transcription
... the training set. For training the stacked RNN models, the training tracks were further divided into sub-sequences of length 200 and the models were trained by Back-Propagation Through Time (BPTT) ...
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Hierarchical Recurrent Neural Network for Document Modeling
... hierarchical recurrent neural network language model (HRNNLM) for document ...two-step training approach is designed, in which sentence-level and word-level lan- guage models are approximated ...
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Deep Learning For Anticipation Of Cardiovascular Disease: A Practical Approach
... Independent Recurrent Neural Network(IndRNN) method can be easy to adjust to deter gradient issues from detonating and disappearing while permitting the network to learn long-term ...robust ...
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Recurrent Neural Network based Rule Sequence Model for Statistical Machine Translation
... ero and 31% for Moses. The results are shown in Table 2. Interestingly the performance of slimmer translation model with fRNN-RSM exceeds baseline with full rule-table, and catches up with the orig- inal fRNN-RSM. 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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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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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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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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Complex Valued Recurrent Neural Network: From Architecture to Training
... ral network and use delayed inputs or 2) use a recurrent architecture and model the dynamics ...Therefore, recurrent archi- tectures are the only sensible way of forecasting dyna- mical systems, ...
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A Connectionist Model of Spatial Knowledge Acquisition in a Virtual Environment
... A sample of 30 postgraduates in the Computer Science Department was asked to perform two tasks within the virtual world, namely exploration and searching. In order to gain familiarity with the environment and learn ...
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