18 citations · 63 across the 18 of their papers we have counts for
10 papers · 1 filter
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…
Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors
Karim Guirguis, Mohamed Abdelsamad, George Eskandar +4
Recent object detection models require large amounts of annotated data for training a new classes of objects. Few-shot object detection (FSOD) aims to address this problem by learn…
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…
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.…
Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version
Razvan-Gabriel Cirstea, Chenjuan Guo, Bin Yang +3
A variety of real-world applications rely on far future information to make decisions, thus calling for efficient and accurate long sequence multivariate time series forecasting. W…
Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning -- Extended Version
Sean Bin Yang, Chenjuan Guo, Jilin Hu +3
In step with the digitalization of transportation, we are witnessing a growing range of path-based smart-city applications, e.g., travel-time estimation and travel path ranking. A…