Sciweavers

ICASSP
2011
IEEE
13 years 4 months ago
Structured Output Layer neural network language model
This paper introduces a new neural network language model (NNLM) based on word clustering to structure the output vocabulary: Structured Output Layer NNLM. This model is able to h...
Hai Son Le, Ilya Oparin, Alexandre Allauzen, Jean-...
BC
2002
85views more  BC 2002»
14 years 9 days ago
Invariant recognition of feature combinations in the visual system
The operation of a hierarchical competitive network model (VisNet) of invariance learning in the visual system is investigated to determine how this class of architecture can solve...
Martin C. M. Elliffe, Edmund T. Rolls, Simon M. St...
TNN
2008
106views more  TNN 2008»
14 years 10 days ago
Unsupervised Segmentation With Dynamical Units
In this paper, we present a novel network to separate mixtures of inputs that have been previously learned. A significant capability of the network is that it segments the componen...
A. Ravishankar Rao, Guillermo A. Cecchi, Charles C...
MS
2003
14 years 1 months ago
Information-theoretic Competitive Learning
— In this paper, we propose a new supervised learning method whereby information is controlled by the associated cost in an intermediate layer, and in an output layer, errors bet...
Ryotaro Kamimura
ICONIP
2008
14 years 1 months ago
Neural Network Regression for LHF Process Optimization
We present a system for regression using MLP neural networks with hyperbolic tangent functions in the input, hidden and output layer. The activation functions in the input and outp...
Miroslaw Kordos
ANNPR
2006
Springer
14 years 2 months ago
A Convolutional Neural Network Tolerant of Synaptic Faults for Low-Power Analog Hardware
Abstract. Recently, the authors described a training method for a convolutional neural network of threshold neurons. Hidden layers are trained by by clustering, in a feed-forward m...
Johannes Fieres, Karlheinz Meier, Johannes Schemme...
ISNN
2005
Springer
14 years 6 months ago
FPGA Realization of a Radial Basis Function Based Nonlinear Channel Equalizer
In this paper we propose a radial basis function (RBF) neural network for nonlinear time-invariant channel equalizer. The RBF network model has a three-layer structure which is com...
Poyueh Chen, Hungming Tsai, ChengJian Lin, ChiYung...