recurrent neural model training
Recurrent Neural Networks for Word Alignment Model
... by training them concurrently (Ma- tusov et ...that model and generaliza- tion errors by the two models differ, and the mod- els must complement each ...our training en- courages word embeddings to ...
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Joint Language and Translation Modeling with Recurrent Neural Networks
... rate training (Och, 2003). Evaluation. We use training and test data from the WMT 2012 campaign and report results on French-English, German-English and English- ...language model based on 1.15bn ...
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Hybrid Data Model Parallel Training for Sequence to Sequence Recurrent Neural Network Machine Translation
... of training time is an important issue in many tasks like patent translation involving neural ...and model parallelism are two com- mon approaches for reducing training time using multiple ...
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Recurrent Neural Network based Rule Sequence Model for Statistical Machine Translation
... translation model to incorpo- rate lexical dependencies that span rule boundaries (Marino et ...sequence model) could help phrase-based translation models to over- come the phrasal independence assumption, ...
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Complex Valued Recurrent Neural Network: From Architecture to Training
... a recurrent architecture and model the dynamics ...Therefore, recurrent archi- tectures are the only sensible way of forecasting dyna- mical systems, ...space model based on the ...
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Enhancing recurrent neural network-based language models by word tokenization
... modified recurrent neural network-based language model for language ...basic recurrent neural network ...smaller training times and memory ...proposed model outperforms ...
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An Intrusion Detection Model based on a Convolutional Neural Network
... Deep Neural Network (DNN) algorithms [7-9], ...detection model using Recurrent Neural Network (RNN) using ...their model with the two kinds of datasets. As training algorithms, ...
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Identification of Artificial Neural Network Models for Three Dimensional Simulation of a Vibration Acoustic Dynamic System
... using recurrent neural networks, where the author presents a learning algorithm for recurrent neural networks based on the Kalman ...two recurrent neural networks: the first ...
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Factored Language Model based on Recurrent Neural Network
... Second, neural networks are notorious for being time consuming during training, future studies will also focus on speeding up the training of factored RNNLM using graphical processing units (Schwenk ...
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A Neural Network based Approach to Automatic Post Editing
... gated recurrent units (GRU) (Cho et ...of training data, LSTMs may lead to better ...for training, we use a full LSTM (as the hid- den units) to model our NNAPE ...
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INTERACTING THROUGH DISCLOSING: PEER INTERACTION PATTERNS BASED ON SELF DISCLOSURE LEVELS VIA FACEBOOK
... DeepSEA model, it only uses a convolutional neural network in their ...this model, the non-coding genomic regions and potential functions of complex disease or trait- associated SNPs are still poorly ...
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Incremental Recurrent Neural Network Dependency Parser with Search based Discriminative Training
... to neural network parsing, RNN models have the advantage that they need minimal feature engineering and therefore they can be used with little effort for a variety of lan- guages and ...target model ...
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Decoder Integration and Expected BLEU Training for Recurrent Neural Network Language Models
... 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 ...use training and test data from the WMT ...
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Human Inspired Neurorobotic System for Classifying Surface Textures by Touch
... a recurrent spiking neural network, using a novel semi-supervised approach for classifying dynamic ...baseline model that does not use the described feature ...
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Restoring speech following total removal of the larynx by a learned transformation from sensor data to acoustics
... for training purposes and the remaining 10% was used for ...the recurrent neu- ral networks trained in that round to predict the corresponding speech ...
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A Neural Approach to Automated Essay Scoring
... on recurrent neural networks to score the essays in an end-to-end ...of neural network models in this pa- per to identify the most suitable ...best model is a long short-term memory ...
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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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Encoding of phonology in a recurrent neural model of grounded speech
... the model can discriminate between phonemes with high ac- curacy across all the layers, and the layer activa- tions are more informative for this task than the MFCC ...the model in this task drops as we ...
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Generative Incremental Dependency Parsing with Neural Networks
... Generative models for graph-based dependency parsing (Eisner, 1996; Wallach et al., 2008) are much less accurate than their discriminative coun- terparts. Syntactic language models based on PCFGs (Roark, 2001; Charniak, ...
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Modeling Skip Grams for Event Detection with Convolutional Neural Networks
... Note that our work is related to (Lei et al., 2015) who employ the non-consecutive convolution for the sentence and news classification problems. Our work is different from (Lei et al., 2015) in that we model the ...
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