Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation; 28th International C Igor V. Tetko,Věra K?rko

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Deference

Vladimir Fridkin,Stephen Ducharmend achieves significant increase in performance on some simple datasets like MNIST. However, CapsNet gets a poor performance on more complex datasets like CIFAR-10. To address this problem, we focus on the improvement of the original CapsNet from both the network structure and the dynamic routing me

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Stability Analysis of a Generalized Class of BAM Neural Networks with Mixed Delaysill construct a new and suitable Lyapunov function to derive the sufficient conditions which ensure that the equilibrium point exist and it is globally exponentially stable. A numerical example is given in order to confirm the theoretical developments of this paper.

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Detection of Directional Information Flow Induced by TMS Based on Symbolic Transfer Entropy-level studies. Most of previous studies have derived functional or effective connectivity or changes in such connectivity during the resting states or cognitive tasks. However, it is difficult to see how such connectivity derived from “passively” recorded data represent actual neural interactions.

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162647.jpg

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https://doi.org/10.1007/978-3-030-30487-4artificial intelligence; classification; clustering; computational linguistics; computer networks; Human-

平項山

978-3-030-30486-7Springer Nature Switzerland AG 2019

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Bidirectional Associative Memory with Block Coding: A Comparison of Iterative Retrieval Methodsde accurate estimates of the maximum pattern number that can be stored at a tolerated noise level of 1%. It is revealed that block coding is most beneficial for sparse activity where each pattern has only . active units.

Tortuous

A Nonlinear Fokker-Planck Description of Continuous Neural Network Dynamicsterizes the model has stationary solutions of the .-MaxEnt type and is associated with a free energy like quantity that decreases during the time-evolution of the system. This framework elucidates a possible dynamical mechanism which can generate .-MaxEnt distributions in Hopfield memory neural netw

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