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530
Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
"... A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real-time. We propose a new computational model for real-time computing on time-var ..."
Abstract
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Cited by 469 (38 self)
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-time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs
Harmonic analysis of neural networks
- Appl. Comput. Harmon. Anal
, 1999
"... It is known that superpositions of ridge functions (single hidden-layer feedforward neural networks) may give good approximations to certain kinds of multivariate functions. It remains unclear, however, how to effectively obtain such approximations. In this paper, we use ideas from harmonic analysis ..."
Abstract
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Cited by 52 (10 self)
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It is known that superpositions of ridge functions (single hidden-layer feedforward neural networks) may give good approximations to certain kinds of multivariate functions. It remains unclear, however, how to effectively obtain such approximations. In this paper, we use ideas from harmonic
Sequence transduction with recurrent neural networks
- in ICML 29
, 2012
"... Many machine learning tasks can be ex-pressed as the transformation—or transduc-tion—of input sequences into output se-quences: speech recognition, machine trans-lation, protein secondary structure predic-tion and text-to-speech to name but a few. One of the key challenges in sequence trans-duction ..."
Abstract
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Cited by 18 (3 self)
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-duction is learning to represent both the in-put and output sequences in a way that is invariant to sequential distortions such as shrinking, stretching and translating. Recur-rent neural networks (RNNs) are a power-ful sequence learning architecture that has proven capable of learning such representa-tions. However
Parameter Space Structure of Continuous-Time Recurrent Neural Networks
, 2006
"... this article (see Figure 1). By transforming equation 2.1 to the output space defined by o #) and setting the time derivative to 0, we find that the SSIO curve of a neuron with self-weight w is given by I # -1 (o) w o. A single additive model neuron can exhibit either unistable or bista ..."
Abstract
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Cited by 27 (3 self)
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this article (see Figure 1). By transforming equation 2.1 to the output space defined by o #) and setting the time derivative to 0, we find that the SSIO curve of a neuron with self-weight w is given by I # -1 (o) w o. A single additive model neuron can exhibit either unistable
Enhanced Beat Tracking with Context-Aware Neural Networks
- in Proceedings of the 14th International Conference on Digital Audio Effects (DAFx-11
, 2011
"... We present two new beat tracking algorithms based on the autocorrelation analysis, which showed state-of-the-art performance in the MIREX 2010 beat tracking contest. Unlike the traditional approach of processing a list of onsets, we propose to use a bidirectional Long Short-Term Memory recurrent neu ..."
Abstract
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Cited by 18 (3 self)
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neural network to perform a frame by frame beat classification of the signal. As inputs to the network the spectral features of the audio signal and their relative differences are used. The network transforms the signal directly into a beat activation function. An autocorrelation function is then used
Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks
- In Proceedings of the International Conference on Machine Learning, ICML 2006
, 2006
"... Many real-world sequence learning tasks re-quire the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is transcribed into words or sub-word units. Recurrent neural networks (RNNs) are powerful sequence learners that would se ..."
Abstract
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Cited by 60 (20 self)
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Many real-world sequence learning tasks re-quire the prediction of sequences of labels from noisy, unsegmented input data. In speech recognition, for example, an acoustic signal is transcribed into words or sub-word units. Recurrent neural networks (RNNs) are powerful sequence learners that would
Abstract Recurrent Neural Networks in Rainfall –Runoff modeling at daily scale
"... This work aims to simulate potential scenarios in Rainfall-Runoff (R-R) transformation at daily scale, mainly perceived for the control and management of water resources, using feed-forward multilayer perceptrons (MLP) and, subsequently, Jordan Recurrent Neural Networks (JNN). R-R transformation is ..."
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This work aims to simulate potential scenarios in Rainfall-Runoff (R-R) transformation at daily scale, mainly perceived for the control and management of water resources, using feed-forward multilayer perceptrons (MLP) and, subsequently, Jordan Recurrent Neural Networks (JNN). R-R transformation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
- IEEE Transactions on Systems, Man and Cybernetics, Part B
"... Abstract—An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO ..."
Abstract
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Cited by 79 (2 self)
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the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent
An on-line self-constructing neural fuzzy inference network and its applications
- IEEE. TRANS. FUZZY. SYS
, 1998
"... A self-constructing neural fuzzy inference network (SONFIN) with on-line learning ability is proposed in this paper. The SONFIN is inherently a modified Takagi–Sugeno–Kang (TSK)-type fuzzy rule-based model possessing neural network’s learning ability. There are no rules initially in the SONFIN. The ..."
Abstract
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Cited by 92 (22 self)
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, to enhance the knowledge representation ability of the SONFIN, a linear transformation for each input variable can be incorporated into the network so that much fewer rules are needed or higher accuracy can be achieved. Proper linear transformations are also learned dynamically in the parameter
Materials for an exploratory theory of the network society.
- The British Journal of Sociology
, 2000
"... ABSTRACT This article aims at proposing some elements for a grounded theor y of the network society. The network society is the social structure characteristic of the Information Age, as tentatively identi ed by empirical, cross-cultural investigation. It permeates most societies in the world, in v ..."
Abstract
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Cited by 122 (0 self)
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around the world, in the Information Age. Since a trend common to many of these transformations refers to the prevalence of information networking as the organizational form of dominant activities, I will then de ne information networks, and elaborate on the implications of networking in social
Results 1 - 10
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