Results 81 to 90 of about 3,974,691 (210)
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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The role of recurrent networks in neural architectures of grounded cognition: learning of control [PDF]
, 2008 Recurrent networks have been used as neural models of language processing, with mixed results. Here, we discuss the role of recurrent networks in a neural architecture of grounded cognition.
Van der Velde, Frank, de Kamps, Marc
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08041 Abstracts Collection – Recurrent Neural Networks - Models, Capacities, and Applications [PDF]
, 2008 From January 20 to 25 2008, the Dagstuhl Seminar 08041 ``Recurrent Neural Networks- Models, Capacities, and Applications'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
Maass, Wolfgang +3 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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Scientific Reports, 2020 In recent years artificial neural networks achieved performance close to or better than humans in several domains: tasks that were previously human prerogatives, such as language processing, have witnessed remarkable improvements in state of the art ...
Andrea Alamia +3 more
doaj +1 more source
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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Robust stability for stochastic Hopfield neural networks with time delays [PDF]
, 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 paper, the asymptotic stability analysis problem is considered for a class of uncertain stochastic ...
Liu, X, Wang, Z, Shu, H, Fang, J
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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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Stochastic graph recurrent neural network
Neurocomputing, 2022 Representation learning over graph structure data has been widely studied due to its wide application prospects. However, previous methods mainly focus on static graphs while many real-world graphs evolve over time. Modeling such evolution is important for predicting properties of unseen networks.
Tijin Yan +3 more
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