Results 31 to 40 of about 75,210 (267)
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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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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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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Network of Recurrent Neural Networks
CoRR, 2017 Under review as a conference paper at AAAI ...
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Bayesian Recurrent Neural Networks
CoRR, 2017 In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by
Meire Fortunato +2 more
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NLOS Identification in WLANs Using Deep LSTM with CNN Features
Sensors, 2018 Identifying channel states as line-of-sight or non-line-of-sight helps to optimize location-based services in wireless communications. The received signal strength identification and channel state information are used to estimate channel conditions for ...
Viet-Hung Nguyen +3 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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