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Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
, 1995
"... Damage to the hippocampal system disrupts recent memory but leaves remote memory intact. The account presented here suggests that memories are first stored via synaptic changes in the hippocampal system, that these changes support reinstatement of recent memories in the neocortex, that neocortical s ..."
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Cited by 399 (36 self)
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Damage to the hippocampal system disrupts recent memory but leaves remote memory intact. The account presented here suggests that memories are first stored via synaptic changes in the hippocampal system, that these changes support reinstatement of recent memories in the neocortex, that neocortical synapses change a little on each reinstatement, and that remote memory is based on accumulated neocortical changes. Models that learn via changes to connections help explain this organization. These models discover the structure in ensembles of items if learning of each item is gradual and interleaved with learning about other items. This suggests that the neocortex learns slowly to discover the structure in ensembles of experiences. The hippocampal system permits rapid learning of new items without disrupting this structure, and reinstatement of new memories interleaves them with others to integrate them into structured neocortical memory systems.
Efficient BackProp
, 1998
"... . The convergence of backpropagation learning is analyzed so as to explain common phenomenon observed by practitioners. Many undesirable behaviors of backprop can be avoided with tricks that are rarely exposed in serious technical publications. This paper gives some of those tricks, and offers expl ..."
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Cited by 125 (24 self)
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. The convergence of backpropagation learning is analyzed so as to explain common phenomenon observed by practitioners. Many undesirable behaviors of backprop can be avoided with tricks that are rarely exposed in serious technical publications. This paper gives some of those tricks, and offers explanations of why they work. Many authors have suggested that secondorder optimization methods are advantageous for neural net training. It is shown that most "classical" secondorder methods are impractical for large neural networks. A few methods are proposed that do not have these limitations. 1 Introduction Backpropagation is a very popular neural network learning algorithm because it is conceptually simple, computationally efficient, and because it often works. However, getting it to work well, and sometimes to work at all, can seem more of an art than a science. Designing and training a network using backprop requires making many seemingly arbitrary choices such as the number ...
Dimension Reduction by Local Principal Component Analysis
, 1997
"... Reducing or eliminating statistical redundancy between the components of highdimensional vector data enables a lowerdimensional representation without significant loss of information. Recognizing the limitations of principal component analysis (PCA), researchers in the statistics and neural networ ..."
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Cited by 99 (0 self)
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Reducing or eliminating statistical redundancy between the components of highdimensional vector data enables a lowerdimensional representation without significant loss of information. Recognizing the limitations of principal component analysis (PCA), researchers in the statistics and neural network communities have developed nonlinear extensions of PCA. This article develops a local linear approach to dimension reduction that provides accurate representations and is fast to compute. We exercise the algorithms on speech and image data, and compare performance with PCA and with neural network implementations of nonlinear PCA. We find that both nonlinear techniques can provide more accurate representations than PCA and show that the local linear techniques outperform neural network implementations.
Convergence Properties of the KMeans Algorithms
"... This paper studies the convergence properties of the well known KMeans clustering algorithm. The KMeans algorithm can be described either as a gradient descent algorithm or by slightly extending the mathematics of the EM algorithm to this hard threshold case. We show that the KMeans algorithm act ..."
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Cited by 83 (2 self)
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This paper studies the convergence properties of the well known KMeans clustering algorithm. The KMeans algorithm can be described either as a gradient descent algorithm or by slightly extending the mathematics of the EM algorithm to this hard threshold case. We show that the KMeans algorithm actually minimizes the quantization error using the very fast Newton algorithm.
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...
Learning Rate Schedules For Faster Stochastic Gradient Search
, 1992
"... . Stochastic gradient descent is a general algorithm that includes LMS, online backpropagation, and adaptive kmeans clustering as special cases. The standard choices of the learning rate j (both adaptive and fixed functions of time) often perform quite poorly. In contrast, our recently proposed cl ..."
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Cited by 42 (0 self)
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. Stochastic gradient descent is a general algorithm that includes LMS, online backpropagation, and adaptive kmeans clustering as special cases. The standard choices of the learning rate j (both adaptive and fixed functions of time) often perform quite poorly. In contrast, our recently proposed class of "search then converge" (STC) learning rate schedules (Darken and Moody, 1990b, 1991) display the theoretically optimal asymptotic convergence rate and a superior ability to escape from poor local minima However, the user is responsible for setting a key parameter. We propose here a new methodology for creating the first automatically adapting learning rates that achieve the optimal rate of convergence. INTRODUCTION The stochastic gradient descent algorithm is \DeltaW (t) = \Gammajr W f [W (t); X(t)]; (1) where j is the learning rate, t is the "time", and X(t) is the independent random exemplar chosen at time t. The purpose of the algorithm is to find a parameter vector W that minim...
Comparison of Optimized Backpropagation Algorithms
 Proc. of ESANN'93, Brussels
, 1993
"... Backpropagation is one of the most famous training algorithms for multilayer perceptrons. Unfortunately it can be very slow for practical applications. Over the last years many improvement strategies have been developed to speed up backpropagation. It's very difficult to compare these different tech ..."
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Cited by 39 (1 self)
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Backpropagation is one of the most famous training algorithms for multilayer perceptrons. Unfortunately it can be very slow for practical applications. Over the last years many improvement strategies have been developed to speed up backpropagation. It's very difficult to compare these different techniques, because most of them have been tested on various specific data sets. Most of the reported results are based on some kind of tiny and artificial training sets like XOR, encoder or decoder. It's very doubtful if these results hold for more complicate practical application. In this report an overview of many different speedup techniques is given. All of them were assessed by a very hard practical classification task, which consists of a big medical data set. As you will see many of these optimized algorithms fail in learning the data set. 1 Introduction This report is intended to summarize our experience using many different speedup techniques for the backpropagation algorithm. We have...
Incremental Dynamic Programming for OnLine Adaptive Optimal Control
, 1994
"... Reinforcement learning algorithms based on the principles of Dynamic Programming (DP) have enjoyed a great deal of recent attention both empirically and theoretically. These algorithms have been referred to generically as Incremental Dynamic Programming (IDP) algorithms. IDP algorithms are intended ..."
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Cited by 20 (2 self)
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Reinforcement learning algorithms based on the principles of Dynamic Programming (DP) have enjoyed a great deal of recent attention both empirically and theoretically. These algorithms have been referred to generically as Incremental Dynamic Programming (IDP) algorithms. IDP algorithms are intended for use in situations where the information or computational resources needed by traditional dynamic programming algorithms are not available. IDP algorithms attempt to find a global solution to a DP problem by incrementally improving local constraint satisfaction properties as experience is gained through interaction with the environment. This class of algorithms is not new, going back at least as far as Samuel's adaptive checkersplaying programs,...