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Found 6 papers, 2 papers with code
Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data
This article proposes a generative adversarial wavelet neural operator (GAWNO) as a novel unsupervised deep learning approach for fault detection and isolation of multivariate time series processes. The GAWNO combines the strengths of wavelet neural operators and generative adversarial networks (GANs) to effectively capture both the temporal distributions and the spatial dependencies among different variables of an underlying system.
Mirage: Model-Agnostic Graph Distillation for Graph Classification
GNNs, like other deep learning models, are data and computation hungry.
FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks
In addition, FRIGATE is robust to frugal sensor deployment, changes in road network connectivity, and temporal irregularity in sensing.
TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control
Due to their complex nonlinear dynamics and batch-to-batch variability, batch processes pose a challenge for process control.
Application of Machine Learning in understanding plant virus pathogenesis: Trends and perspectives on emergence, diagnosis, host-virus interplay and management
Inclusion of high throughput technologies in the field of biology has generated massive amounts of biological data in the recent years.
Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control
Control of batch processes is a difficult task due to their complex nonlinear dynamics and unsteady-state operating conditions within batch and batch-to-batch.
