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Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks.

, , , , , и . CoRR, (2017)

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Stein Tveten

Stein Tveten

Smitha Vishnu

Vishnu Kotrajaras

Wolfgang Tvaruskó

 

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Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks., , , , , и . CoRR, (2017)Re-Imagining data analytics software development., , , , и . ISEC, стр. 20:1-20:5. ACM, (2021)TimeNet: Pre-trained deep recurrent neural network for time series classification., , , , и . ESANN, (2017)Meta-Learning for Few-Shot Time Series Classification., , , , и . COMAD/CODS, стр. 28-36. ACM, (2020)Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models., , , , и . CoRR, (2019)Automatic diagnosis of diabetic retinopathy with the aid of adaptive average filtering with optimized deep convolutional neural network., , , , и . Int. J. Imaging Syst. Technol., 30 (4): 1173-1193 (2020)Online anomaly detection with concept drift adaptation using recurrent neural networks., , , , , , и . COMAD/CODS, стр. 78-87. ACM, (2018)Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder., , , , , , и . CoRR, (2016)Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters., , , , и . AAAI, стр. 10003-10004. AAAI Press, (2019)Meta-Learning for Black-Box Optimization., , , , и . ECML/PKDD (2), том 11907 из Lecture Notes in Computer Science, стр. 366-381. Springer, (2019)