Lazy training of radial basis neural networks [PDF]
, 2006 Proceeding of: 16th International Conference on Artificial Neural Networks, ICANN 2006. Athens, Greece, September 10-14, 2006Usually, training data are not evenly distributed in the input space.
Galván, Inés M. +5 more
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Evolutionary cellular configurations for designing feed-forward neural networks architectures [PDF]
, 2001 Proceeding of: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001In the recent years, the interest to develop automatic methods to determine appropriate architectures of feed-forward ...
Gutiérrez Sánchez, Germán +6 more
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Analyzing the Sensitivity of Deep Neural Networks for Sentiment Analysis: A Scoring Approach
, 2020 Part of IEEE WCCI 2020 is the world’s largest technical event on computational intelligence, featuring the three flagship conferences of the IEEE Computational Intelligence Society (CIS) under one roof: The 2020 International Joint Conference on Neural ...
Wei Emma Zhang +7 more
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Studying the capacity of cellular encoding to generate feedforward neural network topologies [PDF]
, 2004 Proceeding of: IEEE International Joint Conference on Neural Networks, IJCNN 2004, Budapest, 25-29 July 2004Many methods to codify artificial neural networks have been developed to avoid the disadvantages of direct encoding schema, improving the search ...
Gutiérrez Sánchez, Germán +3 more
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Towards neural-symbolic integration: the evolutionary neural logic networks [PDF]
, 2004 This work presents the application of a new methodology for the production of neural logic networks into two real-world problems from the medical domain.
Tsakonas, Athanasios
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Analyzing Echo-state Networks Using Fractal Dimension
, 2022 This work joins aspects of reservoir optimization, information-theoretic optimal encoding, and at its center fractal analysis. We build on the observation that, due to the recursive nature of recurrent neural networks, input sequences appear as fractal ...
Obst, Oliver +3 more
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Deferring the learning for better generalization in radial basis neural networks [PDF]
, 2001 Proceeding of: International Conference Artificial Neural Networks — ICANN 2001. Vienna, Austria, August 21–25, 2001The level of generalization of neural networks is heavily dependent on the quality of the training data.
Galván, Inés M. +5 more
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Approximation in shift-invariant spaces with deep ReLU neural networks
, 2022 We study the expressive power of deep ReLU neural networks for approximating functions in dilated shift-invariant spaces, which are widely used in signal processing, image processing, communications and so on.
Wang, Yang, Li, Zhen, Yang, Yunfei
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Taming the reservoir : feedforward training for recurrent neural networks
, 2012 Recurrent neural networks are successfully used for tasks like time series processing and system identification. Many of the approaches to train these networks, however, are often regarded as too slow, too complicated, or both.
Obst, Oliver +4 more
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Computational inference of neural information flow networks [PDF]
, 2006 This research was supported by a Packard Foundation grant and a US National Science Foundation (NSF) Waterman Award to EDJ, an NSF CAREER grant and an Alfred P.
Tom V. Smulders +20 more
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