3.7k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of SciencesCN257 papers
- Beijing Academy of Quantum Information SciencesCN235 papers
- Peking UniversityCN220 papers
- University of Science and Technology of ChinaCN204 papers
- Tsinghua UniversityCN193 papers
- University of Chinese Academy of SciencesCN143 papers
- University of Hong KongHK135 papers
- Institute of PhysicsPL94 papers
- National University of SingaporeSG94 papers
- Peng Cheng LaboratoryCN94 papers
- Hong Kong University of Science and TechnologyHK93 papers
- Fudan UniversityCN90 papers
13 papers · 2 filters
Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis
Yi Liu, Yuanshao Zhu, James J. Q. Yu
Efficient collaboration between collaborative machine learning and wireless communication technology, forming a Federated Edge Learning (FEEL), has spawned a series of next-generat…
An Adaptive Deep Learning Framework for Day-ahead Forecasting of Photovoltaic Power Generation
Xing Luo, Dongxiao Zhang
Accurate forecasts of photovoltaic power generation (PVPG) are essential to optimize operations between energy supply and demand. Recently, the propagation of sensors and smart met…
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
Renhe Jiang, Du Yin, Zhaonan Wang +7
Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car…
Data Streaming and Traffic Gathering in Mesh-based NoC for Deep Neural Network Acceleration
Binayak Tiwari, Mei Yang, Xiaohang Wang +1
The increasing popularity of deep neural network (DNN) applications demands high computing power and efficient hardware accelerator architecture. DNN accelerators use a large numbe…
Generalization Error Analysis of Neural networks with Gradient Based Regularization
Lingfeng Li, Xue-Cheng Tai, Jiang Yang
We study gradient-based regularization methods for neural networks. We mainly focus on two regularization methods: the total variation and the Tikhonov regularization. Applying the…
Differential-Critic GAN: Generating What You Want by a Cue of Preferences
Yinghua Yao, Yuangang Pan, Ivor W. Tsang +1
This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset posse…
Deep Neural Network Modeling of Unknown Partial Differential Equations in Nodal Space
Zhen Chen, Victor Churchill, Kailiang Wu +1
We present a numerical framework for deep neural network (DNN) modeling of unknown time-dependent partial differential equations (PDE) using their trajectory data. Unlike the recen…
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks
Liang Qu, Huaisheng Zhu, Ruiqi Zheng +2
Imbalanced classification on graphs is ubiquitous yet challenging in many real-world applications, such as fraudulent node detection. Recently, graph neural networks (GNNs) have sh…
Robust discovery of partial differential equations in complex situations
Hao Xu, Dongxiao Zhang
Data-driven discovery of partial differential equations (PDEs) has achieved considerable development in recent years. Several aspects of problems have been resolved by sparse regre…
Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles
Zijian Zhang, Shuai Wang, Yuncong Hong +2
The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles. This paper propos…
Multi-label Classification via Adaptive Resonance Theory-based Clustering
Naoki Masuyama, Yusuke Nojima, Chu Kiong Loo +1
This paper proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian…
A Bayesian Federated Learning Framework with Online Laplace Approximation
Liangxi Liu, Xi Jiang, Feng Zheng +4
Federated learning (FL) allows multiple clients to collaboratively learn a globally shared model through cycles of model aggregation and local model training, without the need to s…
Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method
Yuntian Chen, Dou Huang, Dongxiao Zhang +4
Machine learning models have been successfully used in many scientific and engineering fields. However, it remains difficult for a model to simultaneously utilize domain knowledge…