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1409.3215
Cited By
Sequence to Sequence Learning with Neural Networks
10 September 2014
Ilya Sutskever
Oriol Vinyals
Quoc V. Le
AIMat
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Papers citing
"Sequence to Sequence Learning with Neural Networks"
50 / 4,872 papers shown
Title
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
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Recurrent Network Models for Human Dynamics
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A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion
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Language Understanding for Text-based Games Using Deep Reinforcement Learning
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Tejas D. Kulkarni
Regina Barzilay
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30 Jun 2015
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation
Marijn F. Stollenga
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Marcus Liwicki
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Ozan Irsoy
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End-to-end people detection in crowded scenes
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Tree-structured composition in neural networks without tree-structured architectures
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Christopher D. Manning
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Reading Scene Text in Deep Convolutional Sequences
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Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
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Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
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A cognitive neural architecture able to learn and communicate through natural language
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Pointer Networks
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Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
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Transition-Based Dependency Parsing with Stack Long Short-Term Memory
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Miguel Ballesteros
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Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering
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Jointly Modeling Embedding and Translation to Bridge Video and Language
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Sequence to Sequence -- Video to Text
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25
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Differential Recurrent Neural Networks for Action Recognition
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Compositional Vector Space Models for Knowledge Base Completion
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CNN: A Convolutional Architecture for Word Sequence Prediction
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Neural Responding Machine for Short-Text Conversation
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Denoising Autoencoders for fast Combinatorial Black Box Optimization
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Encoding Source Language with Convolutional Neural Network for Machine Translation
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Describing Videos by Exploiting Temporal Structure
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Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
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