Zobrazeno 1 - 10
of 413
pro vyhledávání: '"QIN, A. K."'
Traffic flow forecasting is a crucial task in intelligent transport systems. Deep learning offers an effective solution, capturing complex patterns in time-series traffic flow data to enable the accurate prediction. However, deep learning models are
Externí odkaz:
http://arxiv.org/abs/2411.03588
Personalized Federated Learning (PFL) is widely employed in IoT applications to handle high-volume, non-iid client data while ensuring data privacy. However, heterogeneous edge devices owned by clients may impose varying degrees of resource constrain
Externí odkaz:
http://arxiv.org/abs/2403.11464
In federated learning (FL), the significant communication overhead due to the slow convergence speed of training the global model poses a great challenge. Specifically, a large number of communication rounds are required to achieve the convergence in
Externí odkaz:
http://arxiv.org/abs/2403.11041
Autor:
Sen, Mrinmay, Qin, A. K., C, Gayathri, N, Raghu Kishore, Chen, Yen-Wei, Raman, Balasubramanian
This paper introduces a new stochastic optimization method based on the regularized Fisher information matrix (FIM), named SOFIM, which can efficiently utilize the FIM to approximate the Hessian matrix for finding Newton's gradient update in large-sc
Externí odkaz:
http://arxiv.org/abs/2403.02833
Traffic incident detection plays a key role in intelligent transportation systems, which has gained great attention in transport engineering. In the past, traditional machine learning (ML) based detection methods achieved good performance under a cen
Externí odkaz:
http://arxiv.org/abs/2402.18167
Autor:
Hu, Binyan, Qin, A. K.
Medical image segmentation (MIS) plays an instrumental role in medical image analysis, where considerable effort has been devoted to automating the process. Currently, mainstream MIS approaches are based on deep neural networks (DNNs), which are typi
Externí odkaz:
http://arxiv.org/abs/2402.07330
Autor:
Hu, Binyan, Qin, A. K.
Medical image segmentation has been significantly advanced by deep learning (DL) techniques, though the data scarcity inherent in medical applications poses a great challenge to DL-based segmentation methods. Self-supervised learning offers a solutio
Externí odkaz:
http://arxiv.org/abs/2402.07119
Machine learning photo-z methods, trained directly on spectroscopic redshifts, provide a viable alternative to traditional template fitting methods but may not generalise well on new data that deviates from that in the training set. In this work, we
Externí odkaz:
http://arxiv.org/abs/2402.00323
The precision of unsupervised point cloud registration methods is typically limited by the lack of reliable inlier estimation and self-supervised signal, especially in partially overlapping scenarios. In this paper, we propose an effective inlier est
Externí odkaz:
http://arxiv.org/abs/2307.14019
Training Physics-Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction
Autor:
Wang, Bo, Qin, A. K., Shafiei, Sajjad, Dia, Hussein, Mihaita, Adriana-Simona, Grzybowska, Hanna
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., those described by partial differential equations (PDEs), into the traini
Externí odkaz:
http://arxiv.org/abs/2307.03920