Results 1  10
of
74
Long Shortterm Memory
, 1995
"... "Recurrent backprop" for learning to store information over extended time intervals takes too long. The main reason is insufficient, decaying error back flow. We briefly review Hochreiter's 1991 analysis of this problem. Then we overcome it by introducing a novel, efficient method called "Long Sho ..."
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Cited by 243 (55 self)
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"Recurrent backprop" for learning to store information over extended time intervals takes too long. The main reason is insufficient, decaying error back flow. We briefly review Hochreiter's 1991 analysis of this problem. Then we overcome it by introducing a novel, efficient method called "Long Short Term Memory" (LSTM). LSTM can learn to bridge minimal time lags in excess of 1000 time steps by enforcing constant error flow through internal states of special units. Multiplicative gate units learn to open and close access to constant error flow. LSTM's update
Learning Stochastic Regular Grammars by Means of a State Merging Method
, 1994
"... We propose a new Mgorithm which allows for the identification of any stochastic deterministic regular language as well as the determination of the probabilities of the strings in the language. The algorithm builds the prefix tree acceptor from the sample set and merges systematically equivaJent stat ..."
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Cited by 136 (12 self)
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We propose a new Mgorithm which allows for the identification of any stochastic deterministic regular language as well as the determination of the probabilities of the strings in the language. The algorithm builds the prefix tree acceptor from the sample set and merges systematically equivaJent states. Experimentally, it proves very fast a.ad the time needed grows only linearly with the size of the sample set.
Input/output hmms for sequence processing
 IEEE Transactions on Neural Networks
, 1996
"... We consider problems of sequence processing and propose a solution based on a discrete state model in order to represent past context. Weintroduce a recurrent connectionist architecture having a modular structure that associates a subnetwork to each state. The model has a statistical interpretation ..."
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Cited by 97 (12 self)
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We consider problems of sequence processing and propose a solution based on a discrete state model in order to represent past context. Weintroduce a recurrent connectionist architecture having a modular structure that associates a subnetwork to each state. The model has a statistical interpretation we call Input/Output Hidden Markov Model (IOHMM). It can be trained by the EM or GEM algorithms, considering state trajectories as missing data, which decouples temporal credit assignment and actual parameter estimation. The model presents similarities to hidden Markov models (HMMs), but allows us to map input sequences to output sequences, using the same processing style as recurrent neural networks. IOHMMs are trained using a more discriminant learning paradigm than HMMs, while potentially taking advantage of the EM algorithm. We demonstrate that IOHMMs are well suited for solving grammatical inference problems on a benchmark problem. Experimental results are presented for the seven Tomita grammars, showing that these adaptive models can attain excellent generalization.
Continual Learning In Reinforcement Environments
, 1994
"... Continual learning is the constant development of complex behaviors with no final end in mind. It is the process of learning ever more complicated skills by building on those skills already developed. In order for learning at one stage of development to serve as the foundation for later learning, a ..."
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Cited by 74 (13 self)
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Continual learning is the constant development of complex behaviors with no final end in mind. It is the process of learning ever more complicated skills by building on those skills already developed. In order for learning at one stage of development to serve as the foundation for later learning, a continuallearning agent should learn hierarchically. CHILD, an agent capable of Continual, Hierarchical, Incremental Learning and Development is proposed, described, tested, and evaluated in this dissertation. CHILD accumulates useful behaviors in reinforcement environments by using the Temporal Transition Hierarchies learning algorithm, also derived in the dissertation. This constructive algorithm generates a hierarchical, higherorder neural network that can be used for predicting contextdependent temporal sequences and can learn sequentialtask benchmarks more than two orders of magnitude faster than competing neuralnetwork systems. Consequently, CHILD can quickly solve complicated non...
Constructing Deterministic FiniteState Automata in Recurrent Neural Networks
 Journal of the ACM
, 1996
"... Recurrent neural networks that are trained to behave like deterministic finitestate automata (DFAs) can show deteriorating performance when tested on long strings. This deteriorating performance can be attributed to the instability of the internal representation of the learned DFA states. The use o ..."
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Cited by 70 (16 self)
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Recurrent neural networks that are trained to behave like deterministic finitestate automata (DFAs) can show deteriorating performance when tested on long strings. This deteriorating performance can be attributed to the instability of the internal representation of the learned DFA states. The use of a sigmoidal discriminant function together with the recurrent structure contribute to this instability. We prove that a simple algorithm can construct secondorder recurrent neural networks with a sparse interconnection topology and sigmoidal discriminant function such that the internal DFA state representations are stable, i.e. the constructed network correctly classifies strings of arbitrary length. The algorithm is based on encoding strengths of weights directly into the neural network. We derive a relationship between the weight strength and the number of DFA states for robust string classification. For a DFA with n states and m input alphabet symbols, the constructive algorithm genera...
Extraction of Rules from Discretetime Recurrent Neural Networks
, 1996
"... The extraction of symbolic knowledge from trained neural networks and the direct encoding of (partial) knowledge into networks prior to training are important issues. They allow the exchange of information between symbolic and connectionist knowledge representations. The focas of this paper is on t ..."
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Cited by 61 (15 self)
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The extraction of symbolic knowledge from trained neural networks and the direct encoding of (partial) knowledge into networks prior to training are important issues. They allow the exchange of information between symbolic and connectionist knowledge representations. The focas of this paper is on the quality of the rules that are extracted from recurrent neural networks. Discretetime recurrent neural networks can be trained to correctly classify strings of a regular language. Rules defining the learned grammar can be extracted from networks in the form of deterministic finitestate automata (DFAs) by applying clustering algorithms in the output space of recurrent state neurons. Our algorithm can extract different finitestate automata that are consistent with a training set from the same network. We compare the generalization performances of these different models and the trained network and we introduce a heuristic that permits us to choose among the consistent DFAs the model which best approximates the learned regular grammar.
Sequential Behavior and Learning in Evolved Dynamical Neural Networks
, 1994
"... This paper explores the use of a realvalued modular genetic algorithm to evolve continuoustime recurrent neural networks capable of sequential behavior and learning. We evolve networks that can generate a fixed sequence of outputs in response to an external trigger occurring at varying intervals o ..."
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Cited by 52 (3 self)
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This paper explores the use of a realvalued modular genetic algorithm to evolve continuoustime recurrent neural networks capable of sequential behavior and learning. We evolve networks that can generate a fixed sequence of outputs in response to an external trigger occurring at varying intervals of time. We also evolve networks that can learn to generate one of a set of possible sequences based upon reinforcement from the environment. Finally, we utilize concepts from dynamical systems theory to understand the operation of some of these evolved networks. A novel feature of our approach is that we assume neither an a priori discretization of states or time nor an a priori learning algorithm that explicitly modifies network parameters during learning. Rather, we merely expose dynamical neural networks to tasks that require sequential behavior and learning and allow the genetic algorithm to evolve network dynamics capable of accomplishing these tasks. 2 1. Introduction Much of the rec...
Noisy Time Series Prediction using a Recurrent Neural Network and Grammatical Inference
 Machine Learning
, 2001
"... Financial forecasting is an example of a signal processing problem which is challenging due to small sample sizes, high noise, nonstationarity, and nonlinearity. Neural networks have been very successful in a number of signal processing applications. We discuss fundamental limitations and inherent ..."
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Cited by 47 (0 self)
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Financial forecasting is an example of a signal processing problem which is challenging due to small sample sizes, high noise, nonstationarity, and nonlinearity. Neural networks have been very successful in a number of signal processing applications. We discuss fundamental limitations and inherent difficulties when using neural networks for the processing of high noise, small sample size signals. We introduce a new intelligent signal processing method which addresses the difficulties. The method proposed uses conversion into a symbolic representation with a selforganizing map, and grammatical inference with recurrent neural networks. We apply the method to the prediction of daily foreign exchange rates, addressing difficulties with nonstationarity, overfitting, and unequal a priori class probabilities, and we find significant predictability in comprehensive experiments covering 5 different foreign exchange rates. The method correctly predicts the direction of change for th...
Natural language grammatical inference with recurrent neural networks
 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 1998
"... This paper examines the inductive inference of a complex grammar with neural networks  specifically, the task considered is that of training a network to classify natural language sentences as grammatical or ungrammatical, thereby exhibiting the same kind of discriminatory power provided by the P ..."
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Cited by 45 (1 self)
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This paper examines the inductive inference of a complex grammar with neural networks  specifically, the task considered is that of training a network to classify natural language sentences as grammatical or ungrammatical, thereby exhibiting the same kind of discriminatory power provided by the Principles and Parameters linguistic framework, or GovernmentandBinding theory. Neural networks are trained, without the division into learned vs. innate components assumed by Chomsky, in an attempt to produce the same judgments as native speakers on sharply grammatical/ungrammatical data. How a recurrent neural network could possess linguistic capability and the properties of various common recurrent neural network architectures are discussed. The problem exhibits training behavior which is often not present with smaller grammars and training was initially difficult. However, after implementing several techniques aimed at improving the convergence of the gradient descent backpropagationthroughtime training algorithm, significant learning was possible. It was found that certain architectures are better able to learn an appropriate grammar. The operation of the networks and their training is analyzed. Finally, the extraction of rules in the form of deterministic finite state automata is investigated.
LEARNING DETERMINISTIC REGULAR GRAMMARS FROM STOCHASTIC SAMPLES IN POLYNOMIAL TIME
, 1999
"... In this paper, the identification of stochastic regular languages is addressed. For this purpose, we propose a class of algorithms which allow for the identification of the structure of the minimal stochastic automaton generating the language. It is shown that the time needed grows only linearly wi ..."
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Cited by 45 (12 self)
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In this paper, the identification of stochastic regular languages is addressed. For this purpose, we propose a class of algorithms which allow for the identification of the structure of the minimal stochastic automaton generating the language. It is shown that the time needed grows only linearly with the size of the sample set and a measure of the complexity of the task is provided. Experimentally, our implementation proves very fast for application purposes.