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RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks
Advances in Neural Information Processing Systems 35, 2022 Published as a conference paper at NeurIPS ...
Leo Kozachkov +2 more
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Advances in Distributed Computing and Artificial Intelligence Journal, 2021 Domain Name System (DNS) is a protocol for converting numeric IP addresses of websites into a human-readable form. With the development of technology, to transfer information, a method like DNS tunneling is used which includes data encryption into DNS ...
Dr. Gopal Sakarkar +6 more
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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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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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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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Research progress on neural network algorithms for mixed gas detection in coal mines
Gong-kuang zidonghua, 2023 When coal mine gas sensors are used for mixed gas detection, there is cross interference between measurement signals. It is difficult to ensure detection accuracy.
JIAO Mingzhi +4 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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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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THE COMPARISON OF ARIMA AND RNN FOR FORECASTING GOLD FUTURES CLOSING PRICES
BarekengIn the financial markets, accurately forecasting the closing prices of gold futures is crucial for investors and analysts. Traditional methods like ARIMA (Autoregressive Integrated Moving Average) have been widely used for this purpose, particularly for ...
Windy Ayu Pratiwi +4 more
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