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Language induction by phase transition in dynamical recognizers
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Learning sequential structure with the real-time recurrent learning algorithm
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Guessing can Outperform Many Long Time Lag Algorithms
Guessing can Outperform Many Long Time Lag Algorithms
Learning long-term dependencies in NARX recurrent neural networks
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Gradient calculations for dynamic recurrent neural networks: a survey
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Unsupervised Learning in LSTM Recurrent Neural Networks
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Learning to Learn Using Gradient Descent
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Learning the Long-Term Structure of the Blues
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Dynamic Noise Annealing for Learning Temporal Sequences with Recurrent Neural Networks
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Improving Long-Term Online Prediction with Decoupled Extended Kalman Filters
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Learning Context Sensitive Languages with LSTM Trained with Kalman Filters
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On the Generalization Ability of Recurrent Networks
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Applying LSTM to Time Series Predictable through Time-Window Approaches
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On the Need for a Neural Abstract Machine
Sequence Learning - Paradigms, Algorithms, and Applications
Generalized Relevance LVQ for Time Series
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A Discrete Probabilistic Memory Model for Discovering Dependencies in Time
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Sequence Learning - Paradigms, Algorithms, and Applications
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Spatiotemporal Connectionist Networks: A Taxonomy and Review
Neural Computation
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Learning to Forget: Continual Prediction with LSTM
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Recurrent Neural Networks for Music Computation
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Expert Systems with Applications: An International Journal
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Evolino: hybrid neuroevolution / optimal linear search for sequence learning
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Spurious valleys in the error surface of recurrent networks: analysis and avoidance
IEEE Transactions on Neural Networks
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Evolving Memory Cell Structures for Sequence Learning
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A Novel Connectionist Network for Solving Long Time-Lag Prediction Tasks
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Fuzzy velocity-based temporal dependency for SVM-driven realistic facial animation
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Developmental neural heterogeneity through coarse-coding regulation
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3d gesture recognition applying long short-term memory and contextual knowledge in a CAVE
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Emotion on the road: necessity, acceptance, and feasibility of affective computing in the car
Advances in Human-Computer Interaction - Special issue on emotion-aware natural interaction
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Sequential constant size compressors for reinforcement learning
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Systematically grounding language through vision in a deep, recurrent neural network
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NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
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ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Learning long term dependencies with recurrent neural networks
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ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Affective Interaction in Natural Environments
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TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Improving keyword spotting with a tandem BLSTM-DBN architecture
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Survey: Reservoir computing approaches to recurrent neural network training
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Keyword spotting exploiting Long Short-Term Memory
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Computer Speech and Language
LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework
Image and Vision Computing
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Selective Recurrent Neural Network
Neural Processing Letters
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AGI'13 Proceedings of the 6th international conference on Artificial General Intelligence
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Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.