Sequence to Sequence Learning with Neural Networks
A Survey and Analysis on Sequence Learning Methodologies and Deep Neural Networks
... intensive-training-based neural networks are not suitable to sequence learning because they cannot fulfill the basic requirements for unsupervised or fully self-adaptive ...machine ...
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Sequence to Sequence Learning with Neural Networks
... input sequence and another for the output sequence, because doing so increases the number model parameters at negligible computational cost and makes it natural to train the LSTM on multiple language pairs ...
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Contextualized Non-Local Neural Networks for Sequence Learning
... Convolutional Neural Network. Graph convolu- tional neural networks (GCNN) have been used to learn the representations of graph structure ...general neural message passing algorithm to pre- ...
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Graph to Sequence Learning using Gated Graph Neural Networks
... The second deficiency is that edge label in- formation is encoded in the form of GGNN pa- rameters in the network. This means that each label will have the same “representation” across all graphs. However, the latent ...
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Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering
... The improvement achieved by R-CNN over C- NN demonstrates that answer sequence learning is able to improve the performance of the answer se- lection in CQA. Because modeling the answer se- quence can enjoy ...
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Double Path Networks for Sequence to Sequence Learning
... based Sequence to Sequence learning (S2S) has made remarkable progress in recent ...Convolutional Neural Networks (CNN) and Self Attention Networks (SAN) are the promi- nent ...
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Bandit Structured Prediction for Neural Sequence to Sequence Learning
... non-linear sequence-to-sequence learning us- ing recurrent neural networks with ...of neural machine translation domain adaptation, we found relative improvements of up to ...
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Efficient Sequence Learning with Group Recurrent Networks
... Recurrent neural networks have achieved state-of-the-art results in many artificial in- telligence tasks, such as language modeling, neural machine translation, speech recognition and so ...
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Exploring Sequence to Sequence Learning in Aspect Term Extraction
... best CRF based methods. The main reason is that Bi-LSTM can learn dependencies between words, and this phenomenon demonstrates that neural networks based methods have bigger ad- vantages than CRF-based ...
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Classical Structured Prediction Losses for Sequence to Sequence Learning
... tional neural networks which enable fast and accu- rate generation (Gehring et ...as sequence-level losses require generating at training ...decoder networks share a simple block structure ...
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Sequence-to-point learning with neural networks for nonintrusive load monitoring
... Deep neural networks have been shown to be a promising approach for these problems, but sliding windows are necessary to handle the long sequences which arise in sig- nal processing problems, which raises ...
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Pervasive Attention: 2D Convolutional Neural Networks for Sequence to Sequence Prediction
... The ConvS2S model we trained has embed- dings of dimension 256, a 16-layers encoder and 12-layers decoder. Each convolution uses 3×1 fil- ters and is followed by a gated linear unit with a total of 2 × 256 channels. ...
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NER for Medical Entities in Twitter using Sequence to Sequence Neural Networks
... bigraph neural network with CRF to improve the annotation of the begin- ning of the entity ...ditional neural network designs inspired by other work on deep learning for NER, and combining external ...
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Sequence-discriminative training of deep neural networks
... The DNN trained on the smaller training set (110 hours) has 5 layers, where each hidden layer has 1200 neurons; the network has 4234 output units. This network is randomly ini- tialized, with the weights drawn from N (0, ...
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NARX neural networks for sequence processing tasks
... of learning : the number that balance between exploration and ...the learning process can be slow ( many epochs required ), but it's not so sensitive to major changes in ...patterns. Learning may be ...
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Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks
... 2.2 Long-Short Term Memory Neural Networks LSTMs are a type of Recurrent Neural Networks capable of learning long-term dependencies. They were introduced by Hochreiter and Schmidhuber ...
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Sequence-to-sequence modeling for graph representation learning
... propose sequence-to-sequence architectures for graph representation learning in both supervised and unsupervised ...recurrent neural networks to encode and decode information from ...
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Still not there? Comparing Traditional Sequence to Sequence Models to Encoder Decoder Neural Networks on Monotone String Translation Tasks
... encoder-decoder neural models and compare them with well- known established ...monotone sequence-to-sequence tasks OCR post-correction, spelling correc- tion, grapheme-to-phoneme conversion, and ...
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Seq2Biseq: Bidirectional Output-wise Recurrent Neural Networks for Sequence Modelling
... We found two solutions to these problems. The first was to train on fixed-length, over- lapping sub-sequences, or segments 5 , truncated from the whole sentences, which did not appear to impair the performances ...
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Neural Sequence to sequence Learning of Internal Word Structure
... Abstract Learning internal word structure has re- cently been recognized as an important step in various multilingual processing tasks and in theoretical language com- ...a neural encoder-decoder model for ...
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