Results 31 to 40 of about 296,489 (278)
Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff modelling [PDF]
Hydrology and Earth System Sciences, 2013 Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall–runoff models.
N. J. de Vos
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Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition
Frontiers in Psychology, 2017 Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in
Courtney J. Spoerer +2 more
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Quaternion Recurrent Neural Networks
CoRR, 2018 ICLR Update - Full ...
Parcollet, Titouan +6 more
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SHS Web of Conferences, 2018 The problems of applying neural network methods for solving problems of preventing cyberthreats to flexible self-organizing network infrastructures of digital economy platforms: vehicle adhoc networks, wireless sensor networks, industrial IoT, “smart ...
Kalinin Maxim +2 more
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Evaluation of the Performance of Feedforward and Recurrent Neural Networks in Active Cancellation of Sound Noise [PDF]
Journal of Intelligent Procedures in Electrical Technology, 2012 Active noise control is based on the destructive interference between the primary noise and generated noise from the secondary source. An antinoise of equal amplitude and opposite phase is generated and combined with the primary noise.
Mehrshad Salmasi, Homayoun Mahdavi-Nasab
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ETRI Journal, 2017 Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection.
Hyoung‐Gook Kim, Jin Young Kim
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Recurrent Neural Network Regularization
CoRR, 2014 We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on
Wojciech Zaremba +2 more
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Sensors, 2023 This paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to
Francisco Rau +6 more
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Interpretation of recurrent neural networks [PDF]
Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop, 2002 This paper addresses techniques for interpretation and characterization of trained recurrent nets for time series problems. In particular, we focus on assessment of effective memory and suggest an operational definition of memory. Further we discuss the evaluation of learning curves.
Pedersen, Morten With +1 more
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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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