Results 221 to 230 of about 115,779 (267)
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The hidden-layer problem revisited
Geophysics, 1984Abstract Analysis of refraction seismic data falls into two classes: traditional head-wave analysis, in which discrete "first arrivals" are mapped in terms of traveltime and distance, and modern waveform analysis, in which the time series recorded at each seismometer is considered in its entirety.
I. J. Won, Michael Bevis
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The hidden layer associative memory model of hippocampus
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000An earlier model introduced by the authors (1999) for fast associative memory has shown to be an efficient solution to the storage of binary patterns and the recall from incomplete input. We extend this model to include more biologically realistic constraints to serve as a model for the hippocampus.
Winfried A. Fellenz, John G. Taylor
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Estimation with two hidden layer neural nets
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003We deal with function estimation by neural networks. Mean square error bounds are given for the case when the target function is in the convex hull of ellipsoids multiplied by a scalar constant. When the target function is not in this class but is bounded, we bound the difference between the mean square prediction error compared to the best ...
Gerald H. L. Cheang, Andrew R. Barron
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A step towards the frontier between one-hidden-layer and two-hidden-layer neural networks
Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), 2005This paper addresses the exact realization of functions, from the d-dimensional affine space to {0,1}, by feedforward multilayer neural networks of threshold units. A classification of the network architectures, according to their number of hidden layers, points out the difficulty of locating the frontier between dichotomies which can be realized with ...
M. Cosnard, P. Koiran, H. Paugam-Moisy
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Microproteins: Uncovering Hidden Layers of the Proteome
Once dismissed as nonfunctional transcriptional noise, small open reading frames (sORFs) and their encoded microproteins have rapidly emerged as key players in diverse biological processes. Ranging from just a few to about 150 amino acids in length, microproteins are now recognized for their ability to modulate cellular functions, often acting as ...Naveen, Shankar, Stephan, Wenkel
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Generalization capability of one and two hidden layers
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003We present an experimental comparison of the generalization capability of one and two hidden layers multilayer feedforward neural networks. We used sixteen different real world problems in order to measure the generalization of both architectures. For each problem and architecture we carefully selected, by a trial and error procedure, the minimal ...
Mercedes Fernández-Redondo +1 more
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Robust Segmentation of Hidden Layers in Video Sequences
18th International Conference on Pattern Recognition (ICPR'06), 2006In this paper, we propose a novel and robust method for extracting motion layers in video sequences. Taking advantage of temporal continuity, our framework considers both the visible and the hidden parts of each layer in order to increase robustness. Moreover, the hidden parts of the layers are recovered, which could be of great help in many high level
Romain Dupont +2 more
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Uncovering the Hidden Layers of the Sun
2020Professor Michael Thompson had made major contributions in developing techniques that have allowed us to ‘invert’ helioseismic data. These analyses have allowed us to determine the structure of the inner layers of the Sun. This in turn has led to tests of solar models, and perhaps more importantly, tests of the properties of stellar material.
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The hidden layer in seismic prospecting
Geologiska Föreningen i Stockholm Förhandlingar, 1974Abstract One condition which has to be fulfilled in seismic-refraction prospecting is an increasing velocity distribution. But even if this condition is met, one intermediate layer may pass undetected. When such a situation occurs, with a hidden layer, it can give rise to considerable errors in the depth determination.
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Generalized Single-Hidden Layer Feedforward Networks
2013In this paper, we propose a novel generalized single-hidden layer feedforward network (GSLFN) by employing polynomial functions of inputs as output weights connecting randomly generated hidden units with corresponding output nodes. The main contributions are as follows.
Ning Wang 0002 +5 more
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