Results 281 to 290 of about 143,911 (316)

CA-LSTM

The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018
Search task identification aims to understand a user's information needs to improve search quality for applications such as query suggestion, personalized search, and advertisement retrieval. To properly identify the search task within long query sessions, it is important to partition these sessions into segments before further processing.
Cong Du, Peng Shu, Yong Li
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E-LSTM

Proceedings of the 56th Annual Design Automation Conference 2019, 2019
Various models with Long Short-Term Memory (LSTM) network have demonstrated prior art performances in sequential information processing. Previous LSTM-specific architectures set large on-chip memory for weight storage to alleviate the memory-bound issue and facilitate the LSTM inference in cloud computing. In this paper, E-LSTM is proposed for embedded
Runbin Shi, Junjie Liu, Hayden Kwok-Hay So, Shuo Wang 0009, Yun Liang 0001   +4 more
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Spatiotemporal Representation Learning with GAN Trained LSTM-LSTM Networks

2020 IEEE International Conference on Robotics and Automation (ICRA), 2020
Learning robot behaviors in unstructured environments often requires handcrafting the features for a given task. In this paper, we present and evaluate an unsupervised representation learning architecture, Layered Spatiotemporal Memory Long Short-Term Memory (LSTM-LSTM), that learns the underlying representation without knowledge of the task.
Yiwei Fu, Shiraj Sen, Johan Reimann, Charles Theurer   +3 more
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SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction

2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018
Pedestrian trajectory prediction is an extremely challenging problem because of the crowdedness and clutter of the scenes. Previous deep learning LSTM-based approaches focus on the neighbourhood influence of pedestrians but ignore the scene layouts in pedestrian trajectory prediction.
Hao Xue 0001, Du Q. Huynh, Mark Reynolds 0001   +2 more
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LSTM-BA: DDoS Detection Approach Combining LSTM and Bayes

2019 Seventh International Conference on Advanced Cloud and Big Data (CBD), 2019
The development of cyberspace brings both opportunities and threats, among which Distributed Denial of Service (DDoS) is one of the most destructive attacks. A mass of DDoS attack detection methods have been proposed. But more or less there are some problems, either the construction process is complex, or low accuracy, or poor generalization ability ...
Yan Li 0085, Yifei Lu 0001
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Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India

2023 6th International Conference on Information Systems and Computer Networks (ISCON), 2023
Satya Prakash, Anand Singh Jalal, Pooja Pathak   +2 more
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AB-LSTM

ACM Transactions on Multimedia Computing, Communications, and Applications, 2019
Detection of scene text in arbitrary shapes is a challenging task in the field of computer vision. Most existing scene text detection methods exploit the rectangle/quadrangular bounding box to denote the detected text, which fails to accurately fit text with arbitrary shapes, such as curved text. In addition, recent progress on scene text detection has
Zhandong Liu, Wengang Zhou 0001, Houqiang Li   +2 more
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Forecasting agricultural commodities prices using deep learning-based models: basic LSTM, bi-LSTM, stacked LSTM, CNN LSTM, and convolutional LSTM

International Journal of Sustainable Agricultural Management and Informatics, 2022
R. Murugesan, Eva Mishra, Akash Hari Krishnan   +2 more
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IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction

IEEE Transactions on Cybernetics
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
Jing Yang 0014, Yuehai Chen, Shaoyi Du, Badong Chen, José C. Príncipe   +4 more
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KAN‐LSTM: A New LSTM Structure for the Prediction of the Stock Market

Concurrency and Computation: Practice and Experience
ABSTRACT Accurate stock market prediction is crucial for investors to formulate correct investment strategies. However, the non‐linearity, high dimensionality, and volatility of financial data pose significant challenges to existing stock market prediction models.
Cheng Zhu, Weiping Zhu 0004, Jin Liu 0016, Yongqiang Tang, Xiao Liu 0004   +4 more
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