Recurrent Neural Network Regularization
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Summary
This paper shows how to correctly apply dropout to LSTMs, and shows that it substantially reduces overfitting on a variety of tasks.
- Type
- preprint
- Published
- 2014-09-08
- Cited by
- 3,033
- References
- 37
- Access
- Open access
- OpenAlex
- https://openalex.org/W1591801644
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17719760
Keywords
Regularization (linguistics), Artificial neural network, Computer science, Artificial intelligence
References
- Regularization of Neural Networks using DropConnect
- Fast dropout training
- Recurrent neural network based language model
- Connectionist Speech Recognition: A Hybrid Approach
- Building a Large Annotated Corpus of English: The Penn Treebank
- Recurrent Continuous Translation Models
- Generating Sequences With Recurrent Neural Networks
- Show and tell: A neural image caption generator
- Strategies for training large scale neural network language models
- Dropout Improves Recurrent Neural Networks for Handwriting Recognition
- Context dependent recurrent neural network language model
- 2007 Special Issue: Optimization and applications of echo state networks with leaky- integrator neurons
- Long Short-Term Memory
- Going deeper with convolutions
- A Novel Connectionist System for Unconstrained Handwriting Recognition
- Exploiting Similarities among Languages for Machine Translation
- BYBLOS: The BBN continuous speech recognition system
- A Clockwork RNN
- Speech recognition with deep recurrent neural networks
- Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cited by
- Reservoir Computing Approaches for Representation and Classification of Multivariate Time Series
- Learning Temporal Embeddings for Complex Video Analysis
- How Well Can a CNN Marginalize Simple Nuisances It is Designed for?
- An Empirical Exploration of Recurrent Network Architectures
- Deep Neural Networks for Large Vocabulary Handwritten Text Recognition. (Réseaux de Neurones Profonds pour la Reconnaissance de Texte Manucrit à Large Vocabulaire)
- Recognizing Functions in Binaries with Neural Networks
- Efficient batchwise dropout training using submatrices
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
- Learning to Execute
- Describing Videos by Exploiting Temporal Structure
- LSTM: A Search Space Odyssey
- End-To-End Memory Networks
- Scalable Bayesian Optimization Using Deep Neural Networks
- Gated Feedback Recurrent Neural Networks
- Qualitatively characterizing neural network optimization problems
- A Primer on Neural Network Models for Natural Language Processing
- Show and tell: A neural image caption generator
- Effective Approaches to Attention-based Neural Machine Translation
- Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
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