18 citations · 70 across the 8 of their papers we have counts for
8 papers
Training Physics-Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction
Bo Wang, A. K. Qin, Sajjad Shafiei +3
Physics-informed neural networks (PINNs) are a newly emerging research frontier in machine learning, which incorporate certain physical laws that govern a given data set, e.g., tho…
TrafFormer: A Transformer Model for Predicting Long-term Traffic
David Alexander Tedjopurnomo, Farhana M. Choudhury, A. K. Qin
Traffic prediction is a flourishing research field due to its importance in human mobility in the urban space. Despite this, existing studies only focus on short-term prediction of…
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks
Binyan Hu, Yu Sun, A. K. Qin
Deep neural networks (DNNs) often rely on massive labelled data for training, which is inaccessible in many applications. Data augmentation (DA) tackles data scarcity by creating n…
AdvDrop: Adversarial Attack to DNNs by Dropping Information
Ranjie Duan, Yuefeng Chen, Dantong Niu +3
Human can easily recognize visual objects with lost information: even losing most details with only contour reserved, e.g. cartoon. However, in terms of visual perception of Deep N…
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink
Ranjie Duan, Xiaofeng Mao, A. K. Qin +4
Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted fro…
DTG-Net: Differentiated Teachers Guided Self-Supervised Video Action Recognition
Ziming Liu, Guangyu Gao, A. K. Qin +1
State-of-the-art video action recognition models with complex network architecture have archived significant improvements, but these models heavily depend on large-scale well-label…
Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles
Ranjie Duan, Xingjun Ma, Yisen Wang +3
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. Existing works have mostly focused on either digital adversarial examples created via small and impe…
An extended catalog of galaxy-galaxy strong gravitational lenses discovered in DES using convolutional neural networks
C. Jacobs, T. Collett, K. Glazebrook +55
We search Dark Energy Survey (DES) Year 3 imaging for galaxy-galaxy strong gravitational lenses using convolutional neural networks, extending previous work with new training sets…