182 citations · 431 across the 40 of their papers we have counts for
11 papers · 2 filters
AutoPINN: When AutoML Meets Physics-Informed Neural Networks
Xinle Wu, Dalin Zhang, Miao Zhang +5
Physics-Informed Neural Networks (PINNs) have recently been proposed to solve scientific and engineering problems, where physical laws are introduced into neural networks as prior…
Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecasting
Yunyao Cheng, Chenjuan Guo, Kaixuan Chen +6
Accurate time series forecasting is crucial for optimizing resource allocation, industrial production, and urban management, particularly with the growth of cyber-physical and IoT…
Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting
Xinle Wu, Dalin Zhang, Miao Zhang +3
Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. The ke…
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis
Yan Zhao, Liwei Deng, Xuanhao Chen +7
The continued digitization of societal processes translates into a proliferation of time series data that cover applications such as fraud detection, intrusion detection, and energ…
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey
Dalin Zhang, Kaixuan Chen, Yan Zhao +3
Deep learning technologies have demonstrated remarkable effectiveness in a wide range of tasks, and deep learning holds the potential to advance a multitude of applications, includ…
TTAPS: Test-Time Adaption by Aligning Prototypes using Self-Supervision
Alexander Bartler, Florian Bender, Felix Wiewel +1
Nowadays, deep neural networks outperform humans in many tasks. However, if the input distribution drifts away from the one used in training, their performance drops significantly.…