Neural Computation
SCIE-ISI SCOPUS (1995-2023)
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Cơ quản chủ quản: MIT PRESS , MIT Press Journals
Lĩnh vực:
Cognitive NeuroscienceArts and Humanities (miscellaneous)
Các bài báo tiêu biểu
Long Short-Term Memory 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 ... ...
Tập 9 Số 8 - Trang 1735-1780 - 1997
A Fast Learning Algorithm for Deep Belief Nets We show how to use “complementary priors” to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy a... ...
Tập 18 Số 7 - Trang 1527-1554 - 2006
Backpropagation Applied to Handwritten Zip Code Recognition The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A singl... ...
Tập 1 Số 4 - Trang 541-551 - 1989
An Information-Maximization Approach to Blind Separation and Blind Deconvolution We derive a new self-organizing learning algorithm that maximizes the information transferred in a network of nonlinear units. The algorithm does not assume any knowledge of the input distributions, and is defined here for the zero-noise limit. Under these conditions, information maximization has extra properties not found in the linear case (Linsker 1989). The nonlinearities in the trans... ...
Tập 7 Số 6 - Trang 1129-1159 - 1995
Nonlinear Component Analysis as a Kernel Eigenvalue Problem A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map—for instance, the space of all possible five-pixel products in 16 × 16 images. We give the derivation of the method and p... ...
Tập 10 Số 5 - Trang 1299-1319 - 1998
Fast Learning in Networks of Locally-Tuned Processing Units We propose a network architecture which uses a single internal layer of locally-tuned processing units to learn both classification tasks and real-valued function approximations (Moody and Darken 1988). We consider training such networks in a completely supervised manner, but abandon this approach in favor of a more computationally efficient hybrid learning method which combines self-orga... ...
Tập 1 Số 2 - Trang 281-294 - 1989
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks The exact form of a gradient-following learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have (1) the advantage that they do not require a precisely defined training interval, operating while the network runs; and (2) the disadvantage th... ...
Tập 1 Số 2 - Trang 270-280 - 1989
Bayesian Interpolation Although Bayesian analysis has been in use since Laplace, the Bayesian method of model-comparison has only recently been developed in depth. In this paper, the Bayesian approach to regularization and model-comparison is demonstrated by studying the inference problem of interpolating noisy data. The concepts and methods described are quite general and can be applied to many other data mode... ...
Tập 4 Số 3 - Trang 415-447 - 1992
Adaptive Mixtures of Local Experts We present a new supervised learning procedure for systems composed of many separate networks, each of which learns to handle a subset of the complete set of training cases. The new procedure can be viewed either as a modular version of a multilayer supervised network, or as an associative version of competitive learning. It therefore provides a new link between these two apparently diffe... ...
Tập 3 Số 1 - Trang 79-87 - 1991
Universal Approximation Using Radial-Basis-Function Networks There have been several recent studies concerning feedforward networks and the problem of approximating arbitrary functionals of a finite number of real variables. Some of these studies deal with cases in which the hidden-layer nonlinearity is not a sigmoid. This was motivated by successful applications of feedforward networks with nonsigmoidal hidden-layer units. This p... ...
Tập 3 Số 2 - Trang 246-257 - 1991