Results 51 to 60 of about 60,490 (168)
CoRR, 2017 Recurrent neural networks like long short-term memory (LSTM) are important architectures for sequential prediction tasks. LSTMs (and RNNs in general) model sequences along the forward time direction. Bidirectional LSTMs (Bi-LSTMs) on the other hand model sequences along both forward and backward directions and are generally known to perform better at ...
Samira Shabanian +3 more
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IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction
CoRR, 2023 Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians around is beneficial for policy decision to avoid collision. It is a challenging issue because humans have different walking motions, and the interactions between humans and
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Scientific ReportsWind noise impairs the functionality of hearing aids and hearables outdoors or during sports by interfering with communication signals. This study aims to visualize the wind noise generation patterns around the human head by validated scale-resolved flow
Jörg Riedel +4 more
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Scientific ReportsEscalating insecticide resistance threatens the efficacy of LLINs, undermining malaria control in Africa. We conducted the first experimental hut trials in Uganda using highly resistant free-flying wild Anopheles mosquitoes and F2 hybrids of FANG and ...
Ambrose Oruni +8 more
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CoRR, 2015 LSTM (Long Short-Term Memory) recurrent neural networks have been highly successful in a number of application areas. This technical report describes the use of the MNIST and UW3 databases for benchmarking LSTM networks and explores the effect of different architectural and hyperparameter choices on performance.
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SR-LSTM: State Refinement for LSTM Towards Pedestrian Trajectory Prediction [PDF]
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 In crowd scenarios, reliable trajectory prediction of pedestrians requires insightful understanding of their social behaviors. These behaviors have been well investigated by plenty of studies, while it is hard to be fully expressed by hand-craft rules. Recent studies based on LSTM networks have shown great ability to learn social behaviors.
Pu Zhang 0001 +4 more
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EA-LSTM: Evolutionary attention-based LSTM for time series prediction [PDF]
Knowledge-Based Systems, 2019 Time series prediction with deep learning methods, especially long short-term memory neural networks (LSTMs), have scored significant achievements in recent years. Despite the fact that the LSTMs can help to capture long-term dependencies, its ability to pay different degree of attention on sub-window feature within multiple time-steps is insufficient.
Youru Li +4 more
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Jasmonates—the Master Regulator of Rice Development, Adaptation and Defense
Plants, 2019 Rice is one of the most important food crops worldwide, as well as the model plant in molecular studies on the cereals group. Many different biotic and abiotic agents often limit rice production and threaten food security.
Hieu Trang Nguyen +4 more
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LSTM-Based Whisper Detection [PDF]
2018 IEEE Spoken Language Technology Workshop (SLT), 2018 This article presents a whisper speech detector in the far-field domain. The proposed system consists of a long-short term memory (LSTM) neural network trained on log-filterbank energy (LFBE) acoustic features. This model is trained and evaluated on recordings of human interactions with voice-controlled, far-field devices in whisper and normal ...
Zeynab Raeesy +7 more
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BMC Infectious Diseases, 2017 Background Low-dose primaquine is a key candidate for use in malaria transmission reduction and elimination campaigns such as mass drug administration (MDA).
Daniel Joseph Hayes +3 more
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