Results 41 to 50 of about 99,773 (316)
Learning extreme vegetation response to climate drivers with recurrent neural networks [PDF]
Nonlinear Processes in GeophysicsThe spectral signatures of vegetation are indicative of ecosystem states and health. Spectral indices used to monitor vegetation are characterized by long-term trends, seasonal fluctuations, and responses to weather anomalies. This study investigates the
F. Martinuzzi +13 more
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SORN : a self-organizing recurrent neural network
, 2009 Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing ...
Pipa, Gordon +5 more
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Network of Recurrent Neural Networks
CoRR, 2017 Under review as a conference paper at AAAI ...
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Generalised Analog LSTMs Recurrent Modules for Neural Computing
Frontiers in Computational Neuroscience, 2021 The human brain can be considered as a complex dynamic and recurrent neural network. There are several models for neural networks of the human brain, that cover sensory to cortical information processing. Large majority models include feedback mechanisms
Kazybek Adam +2 more
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Relational recurrent neural networks
CoRR, 2018 Memory-based neural networks model temporal data by leveraging an ability to remember information for long periods. It is unclear, however, whether they also have an ability to perform complex relational reasoning with the information they remember.
Adam Santoro +9 more
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Dilated Recurrent Neural Networks
CoRR, 2017 Learning with recurrent neural networks (RNNs) on long sequences is a notoriously difficult task. There are three major challenges: 1) complex dependencies, 2) vanishing and exploding gradients, and 3) efficient parallelization. In this paper, we introduce a simple yet effective RNN connection structure, the DilatedRNN, which simultaneously tackles all
Shiyu Chang +9 more
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Exploring Efficient Neural Architectures for Linguistic–Acoustic Mapping in Text-To-Speech
Applied Sciences, 2019 Conversion from text to speech relies on the accurate mapping from linguistic to acoustic symbol sequences, for which current practice employs recurrent statistical models such as recurrent neural networks. Despite the good performance of such models (in
Santiago Pascual +2 more
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Neural Approximators for Variable-Order Fractional Calculus Operators (VO-FC)
IEEE Access, 2022 The paper presents research on the approximation of variable-order fractional operators by recurrent neural networks. The research focuses on two basic variable-order fractional operators, i.e., integrator and differentiator.
Bartosz Puchalski
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Linked Recurrent Neural Networks
CoRR, 2018 Recurrent Neural Networks (RNNs) have been proven to be effective in modeling sequential data and they have been applied to boost a variety of tasks such as document classification, speech recognition and machine translation. Most of existing RNN models have been designed for sequences assumed to be identically and independently distributed (i.i.d ...
Zhiwei Wang 0001 +3 more
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Exponential stability of delayed recurrent neural networks with Markovian jumping parameters
, 2006 This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.In this Letter, the global exponential stability analysis problem is considered for a class of recurrent ...
Liu, Y, Yu, L, Wang, Z, Iu, X
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