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Markovian architectural bias of recurrent neural networks.

, , и . IEEE Trans. Neural Networks, 15 (1): 6-15 (2004)

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Lubica Fecurka

 

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Approaches Based on Markovian Architectural Bias in Recurrent Neural Networks., , и . SOFSEM, том 2932 из Lecture Notes in Computer Science, стр. 257-264. Springer, (2004)Neural Networks-Based Parametric Testing of Analog IC., , , и . DFT, стр. 408-418. IEEE Computer Society, (2002)Building Predictive Models on Complex Symbolic Sequences with a Second-Order Recurrent BCM Network with Lateral Inhibition., , и . IJCNN (2), стр. 265-270. IEEE Computer Society, (2000)0-7695-0619-4.Analysis and Visualization of the Dynamics of Recurrent Neural Networks for Symbolic Sequences Processing., и . ICANN (2), том 5164 из Lecture Notes in Computer Science, стр. 577-586. Springer, (2008)Modeling Plasticity in Rat Barrel Cortex Induced by One Spared Whisker.. ICANN, том 1327 из Lecture Notes in Computer Science, стр. 127-132. Springer, (1997)Training Recurrent Connectionist Models on Symbolic Time Series., и . ICONIP (1), том 5506 из Lecture Notes in Computer Science, стр. 285-292. Springer, (2008)Topology of Brain Functional Networks: Towards the Role of Genes., , и . ICONIP (1), том 5506 из Lecture Notes in Computer Science, стр. 111-118. Springer, (2008)Meta-STDP Rule Stabilizes Synaptic Weights Under in Vivo-like Ongoing Spontaneous Activity in a Computational Model of CA1 Pyramidal Cell., , и . ICANN (2), том 12397 из Lecture Notes in Computer Science, стр. 670-680. Springer, (2020)Finite-State Reber Automaton and the Recurrent Neural Networks Trained in Supervised and Unsupervised Manner., и . ICANN, том 2130 из Lecture Notes in Computer Science, стр. 737-742. Springer, (2001)Markovian architectural bias of recurrent neural networks., , и . IEEE Trans. Neural Networks, 15 (1): 6-15 (2004)