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Generative AI in structure-based drug discovery. [PDF]
BMC BiolZhong Z, Durrant JD.
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Air quality estimation from sequential surveillance images using a unified CNN-RNN framework. [PDF]
iScienceWang X, Liu X, Mao W.
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MM-RNN: A Multimodal RNN for Precipitation Nowcasting
IEEE Transactions on Geoscience and Remote Sensing, 2023Hao Zhang, Zhifeng Ma
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HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summarization
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018Although video summarization has achieved great success in recent years, few approaches have realized the influence of video structure on the summarization results. As we know, the video data follow a hierarchical structure, i.e., a video is composed of shots, and a shot is composed of several frames.
Bin Zhao 0001 +2 more
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Prosodic Break Prediction with RNNs
2016Prosodic breaks prediction from text is a fundamental task to obtain naturalness in text to speech applications. In this work we build a data-driven break predictor out of linguistic features like the Part of Speech (POS) tags and forward-backward word distance to punctuation marks, and to do so we use a basic Recurrent Neural Network (RNN) model to ...
Pascual de la Puente, Santiago +1 more
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CAM-RNN: Co-Attention Model Based RNN for Video Captioning
IEEE Transactions on Image Processing, 2019Video captioning is a technique that bridges vision and language together, for which both visual information and text information are quite important. Typical approaches are based on the recurrent neural network (RNN), where the video caption is generated word by word, and the current word is predicted based on the visual content and previously ...
Bin Zhao 0001 +2 more
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RNNs for Classification of Driving Behaviour
2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), 2019Recurrent neural networks are an obvious choice for driving behavior analysis by means of time series of measurements, obtained either from telematics or mobile phone sensors. This work investigates such an application, employing two popular recurrent neural networks, i.e. long short-term memory networks and gated recurrent unit networks, as well as 1D
Dimitris Mantzekis +3 more
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2019 IEEE International Workshop on Signal Processing Systems (SiPS), 2019
The task of rain detection, also known as wet-dry classification, using recurrent neural networks (RNNs) utilizing data from commercial microwave links (CMLs) has recently gained attention. Whereas previous studies used long short-term memory (LSTM) units, here we used gated recurrent units (GRUs).
Hai Victor Habi, Hagit Messer
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The task of rain detection, also known as wet-dry classification, using recurrent neural networks (RNNs) utilizing data from commercial microwave links (CMLs) has recently gained attention. Whereas previous studies used long short-term memory (LSTM) units, here we used gated recurrent units (GRUs).
Hai Victor Habi, Hagit Messer
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2020
In this chapter, we will introduce the typical deep neural networks from the viewpoint of CNN family, especially region-based CNN, SSD, and YOLO. Meanwhile, from the viewpoint of time series analysis, we depict the RNN family, namely, LSTM, GRU, FRU, etc.
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In this chapter, we will introduce the typical deep neural networks from the viewpoint of CNN family, especially region-based CNN, SSD, and YOLO. Meanwhile, from the viewpoint of time series analysis, we depict the RNN family, namely, LSTM, GRU, FRU, etc.
openaire +1 more source
2018
This chapter will discuss the concepts of recurrent neural networks (RNNs) and their modified version, long short-term memory (LSTM). LSTM is mainly used for sequence prediction. You will learn about the varieties of sequence prediction and then learn how to do time-series forecasting with the help of the LSTM model.
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This chapter will discuss the concepts of recurrent neural networks (RNNs) and their modified version, long short-term memory (LSTM). LSTM is mainly used for sequence prediction. You will learn about the varieties of sequence prediction and then learn how to do time-series forecasting with the help of the LSTM model.
openaire +1 more source