676 citations · 4.7k across the 170 of their papers we have counts for
14 papers · 1 filter
Road Network Guided Fine-Grained Urban Traffic Flow Inference
Lingbo Liu, Mengmeng Liu, Guanbin Li +3
Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem, which can help greatly reduce the number of the required traffic monitor…
Prototypical Graph Contrastive Learning
Shuai Lin, Pan Zhou, Zi-Yuan Hu +6
Graph-level representations are critical in various real-world applications, such as predicting the properties of molecules. But in practice, precise graph annotations are generall…
Unsupervised Multi-view Clustering by Squeezing Hybrid Knowledge from Cross View and Each View
Junpeng Tan, Yukai Shi, Zhijing Yang +2
Multi-view clustering methods have been a focus in recent years because of their superiority in clustering performance. However, typical traditional multi-view clustering algorithm…
Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction
Lingbo Liu, Jingwen Chen, Hefeng Wu +3
Due to the widespread applications in real-world scenarios, metro ridership prediction is a crucial but challenging task in intelligent transportation systems. However, conventiona…
Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction
Lingbo Liu, Jiajie Zhen, Guanbin Li +4
As a crucial component in intelligent transportation systems, traffic flow prediction has recently attracted widespread research interest in the field of artificial intelligence (A…
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
Ziliang Chen, Zhanfu Yang, Xiaoxi Wang +4
A broad range of cross--domain generation researches boil down to matching a joint distribution by deep generative models (DGMs). Hitherto algorithms excel in pairwise domains w…
Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks
Ziliang Chen, Jingyu Zhuang, Xiaodan Liang +1
(Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training. Learning domain-in…
Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction
Lingbo Liu, Zhilin Qiu, Guanbin Li +3
Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of…
NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning
Sirui Xie, Junning Huang, Lanxin Lei +4
Reinforcement learning agents need exploratory behaviors to escape from local optima. These behaviors may include both immediate dithering perturbation and temporally consistent ex…
SNAS: Stochastic Neural Architecture Search
Sirui Xie, Hehui Zheng, Chunxiao Liu +1
We propose Stochastic Neural Architecture Search (SNAS), an economical end-to-end solution to Neural Architecture Search (NAS) that trains neural operation parameters and architect…
Attentive Crowd Flow Machines
Lingbo Liu, Ruimao Zhang, Jiefeng Peng +3
Traffic flow prediction is crucial for urban traffic management and public safety. Its key challenges lie in how to adaptively integrate the various factors that affect the flow ch…
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
Ruijia Xu, Ziliang Chen, Wangmeng Zuo +2
Unsupervised domain adaptation (UDA) conventionally assumes labeled source samples coming from a single underlying source distribution. Whereas in practical scenario, labeled data…
F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation
Xiaohe Wu, Wangmeng Zuo, Yuanyuan Zhu +1
The generalization error bound of support vector machine (SVM) depends on the ratio of radius and margin, while standard SVM only considers the maximization of the margin but ignor…
Iterated Support Vector Machines for Distance Metric Learning
Wangmeng Zuo, Faqiang Wang, David Zhang +4
Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for cl…