[PDF] Top 20 A recurrent neural network approach to quantitatively studying
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A recurrent neural network approach to quantitatively studying
... and T m and T p are the measured and predicted TEC, T m and T p are their respective means over a length interval of the to- tal number of observations, N under consideration. Table 1 shows the RMSE values obtained for ... See full document
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Recurrent Neural Network Grammars
... introduced recurrent neural network grammars, a probabilistic model of phrase-structure trees that can be trained generatively and used as a language model or a parser, and a corresponding ... See full document
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A General Recurrent Neural Network Approach to Model Genetic Regulatory Networks
... of recurrent neural network models to re- verse-engineering gene networks has been proposed by oth- ers, to date, the design and simulation of recurrent network models have not been ... See full document
5
Unsupervised Recurrent Neural Network Grammars
... hybrid approach where we train a supervised RNNG first and continue fine-tuning the model (including the inference network) on the URNNG objective (RNNG → URNNG in Table ...This approach results in ... See full document
13
Video Classification with Recurrent Neural Network
... with Recurrent Neural Network system will helps to recognize videos from specific class ...hybrid approach for classification as a more powerful technique for video ... See full document
8
Uncertainty in Recurrent Neural Network with Dropout
... single input dimension; for this case, we may lose the only available input information. We must handle this case programmatically, to prevent such dropout. An advantage of the method is, it is easy to extend from ... See full document
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An Approach to Predict a Student’s Academic Performance using Recurrent Neural Network (RNN)
... paper, Recurrent Neural Network (RNN) is used to predict a student’s final ...of neural network that can handle time series ...Artificial Neural Network and Deep ... See full document
5
Hierarchical Recurrent Neural Network for Document Modeling
... hierarchical recurrent neural network language model (HRNNLM) for document ...training approach is designed, in which sentence-level and word-level lan- guage models are approximated for the ... See full document
9
Lipreading with convolutional and recurrent neural network models
... top-down approach, where a priori informa- tion and assumptions are embedded into a model, and the features consist of the model parameter fitted to the image ...bottom-up approach, where features are ... See full document
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Paraphrastic recurrent neural network language models
... similar approach may also be used at the input layer when a large vocabulary is ...alternative network architectures that model a full vocabu- lary at the output layer can be ... See full document
5
Discovering gated recurrent neural network architectures
... LSTM Network Structure and Weights using Unsupervised Objective - InfoMax In this chapter, NEAT (Neuroevolution of Augmenting Topologies) Stanley and Miikkulainen (2002) algorithm is extended to incorporate LSTM ... See full document
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MoL 2018 33: Learning to Decide a Formal Language: A Recurrent Neural Network Approach
... Again, GRU cells can be stacked, yielding multilayer GRU networks. 3.2.5 One-hot and Dense Encodings Note that RNNs work exclusively with numerical data, i.e. real numbers. Suppose that we are given an alphabet A = {a 1 ... See full document
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Distributed representations of action sequences in anterior cingulate cortex : a recurrent neural network approach
... per network = 50 variables; three sequences with seven steps per sequence across 200 networks = 4,200 ...the network through a sequence of seven events, begin- ning with the initial sequence cue (S) ... See full document
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Sentylic at IEST 2018: Gated Recurrent Neural Network and Capsule Network Based Approach for Implicit Emotion Detection
... this approach yielded a reason- able accuracy, yet it was not the best performing ...Another approach is to use a Convolution Neu- ral Network (CNN) (Kim, 2014) layer on top of RNNs instead of ... See full document
6
Software Reliability Prediction using Fuzzy Min-Max Algorithm and Recurrent Neural Network Approach
... 1) Network optimization, 2) Reliability ...the neural network ...the network is ...the network is trained with input and output data proved to the ...the neural network ... See full document
10
Recurrent Neural Network based Translation Quality Estimation
... 1734. Association for Computational Linguistics. Hyun Kim and Jong-Hyeok Lee. 2016. A recurrent neural networks approach for estimating the quality of machine translation output. In Proceedings of ... See full document
6
Translation Quality Estimation using Recurrent Neural Network
... RNN-LMs have been found very effective in different Natural Language Processing (NLP) applications. RNN-LM is mainly used for vector space language model- ing for different NLP problems. For this task, we modify the ... See full document
6
Explaining Recurrent Neural Network Predictions in Sentiment Analysis
... feed-forward neural network classification ...to recurrent neural ...in recurrent network architectures such as LSTMs and ...and quantitatively, obtaining better results ... See full document
10
Recurrent Neural Network with Word Embedding for Complaint Classification
... Pathumthani, Thailand [email protected] Abstract Complaint classification aims at using information to deliver greater insights to enhance user experience after purchasing the products or services. ... See full document
8
Larger Context Language Modelling with Recurrent Neural Network
... fusion approach to a recur- rent language model based on long short- term memory units (LSTM), which helps the LSTM unit keep intra-sentence depen- dencies and inter-sentence dependencies separate from each ... See full document
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