Results 21 to 30 of about 31,169 (266)
Explainability and Adversarial Robustness for RNNs [PDF]
2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService), 2020 Accepted at IEEE BigDataService ...
Alexander Hartl +3 more
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Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release
Iberoamerican Journal of Medicine, 2020 Introduction: Corona Virus Infectious Disease (COVID-19) is the infectious disease. The COVID-19 disease came to earth in early 2019. It is expanding exponentially throughout the world and affected an enormous number of human beings starting from the ...
Shawni Dutta, Samir Kumar Bandyopadhyay
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CoRR, 2016 Many time series are generated by a set of entities that interact with one another over time. This paper introduces a broad, flexible framework to learn from multiple inter-dependent time series generated by such entities. Our framework explicitly models the entities and their interactions through time.
Ashish Bora, Sugato Basu, Joydeep Ghosh
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LSTM based Ensemble Network to enhance the learning of long-term dependencies in chatbot
International Journal for Simulation and Multidisciplinary Design Optimization, 2020 A chatbot is a software that can reproduce a discussion portraying a specific dimension of articulation among people and machines utilizing Natural Human Language.
Patil Shruti +3 more
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Finetuning Pretrained Transformers into RNNs [PDF]
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021 EMNLP ...
Jungo Kasai +8 more
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Design and research of high-speed channel estimation algorithm in OFDM system based on GCE-RNN algorithm [PDF]
Journal of Applied Research on Industrial EngineeringDue to the presence of Inter-Carrier Interference (ICI) and Inter-Symbol Interference (ISI) in Orthogonal Frequency Division Multiplexing (OFDM) systems under high-velocity mobility conditions, channel estimation algorithms are challenged.
Ling Yao +2 more
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On the Computational Power of RNNs
CoRR, 2019 Recent neural network architectures such as the basic recurrent neural network (RNN) and Gated Recurrent Unit (GRU) have gained prominence as end-to-end learning architectures for natural language processing tasks. But what is the computational power of such systems?
Samuel A. Korsky, Robert C. Berwick
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Breast Cancer Disease Prediction With Recurrent Neural Networks (RNN) [PDF]
International Journal of Industrial Engineering and Production Research, 2020 Cancer is a consortium of diseases which comprises abnormal increase in cells growth by having potential to occupy and attack the entire body. According to study breast cancer is the most likely occurs in the women and which became the second biggest ...
sangapu venkata appaji +3 more
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CoRRThe advent of Transformers marked a significant breakthrough in sequence modelling, providing a highly performant architecture capable of leveraging GPU parallelism. However, Transformers are computationally expensive at inference time, limiting their applications, particularly in low-resource settings (e.g., mobile and embedded devices).
Leo Feng +5 more
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Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm
CoRR, 2019 Due to their prevalence, time series forecasting is crucial in multiple domains. We seek to make state-of-the-art forecasting fast, accessible, and generalizable. ES-RNN is a hybrid between classical state space forecasting models and modern RNNs that achieved a 9.4% sMAPE improvement in the M4 competition. Crucially, ES-RNN implementation requires per-
Andrew Redd, Kaung Khin, Aldo Marini
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