219 citations · 1.2k across the 35 of their papers we have counts for
14 papers · 1 filter
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
Xiyue Zhang, Chao Huang, Yong Xu +5
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public ri…
Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network
Lianghao Xia, Chao Huang, Yong Xu +3
Capturing users' precise preferences is of great importance in various recommender systems (eg., e-commerce platforms), which is the basis of how to present personalized interestin…
Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation
Lianghao Xia, Chao Huang, Yong Xu +5
Accurate user and item embedding learning is crucial for modern recommender systems. However, most existing recommendation techniques have thus far focused on modeling users' prefe…
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation
Chao Huang, Jiahui Chen, Lianghao Xia +6
Session-based recommendation plays a central role in a wide spectrum of online applications, ranging from e-commerce to online advertising services. However, the majority of existi…
Knowledge-aware Coupled Graph Neural Network for Social Recommendation
Chao Huang, Huance Xu, Yong Xu +7
Social recommendation task aims to predict users' preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborat…
Graph Meta Network for Multi-Behavior Recommendation
Lianghao Xia, Yong Xu, Chao Huang +2
Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions. In practical recommendation scenarios, use…
Social Recommendation with Self-Supervised Metagraph Informax Network
Xiaoling Long, Chao Huang, Yong Xu +4
In recent years, researchers attempt to utilize online social information to alleviate data sparsity for collaborative filtering, based on the rationale that social networks offers…
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization
Qingsong Zhang, Bin Gu, Cheng Deng +4
Vertical federated learning (VFL) is an effective paradigm of training the emerging cross-organizational (e.g., different corporations, companies and organizations) collaborative l…
Memory-Augmented Non-Local Attention for Video Super-Resolution
Jiyang Yu, Jingen Liu, Liefeng Bo +1
In this paper, we propose a novel video super-resolution method that aims at generating high-fidelity high-resolution (HR) videos from low-resolution (LR) ones. Previous methods pr…
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
Bo Liu, Chaowei Tan, Jiazhou Wang +7
In this paper, we present Fedlearn-Algo, an open-source privacy preserving machine learning platform. We use this platform to demonstrate our research and development results on pr…
Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark
Longyin Wen, Dawei Du, Pengfei Zhu +4
To promote the developments of object detection, tracking and counting algorithms in drone-captured videos, we construct a benchmark with a new drone-captured largescale dataset, n…
Data Augmentation for Object Detection via Differentiable Neural Rendering
Guanghan Ning, Guang Chen, Chaowei Tan +3
It is challenging to train a robust object detector under the supervised learning setting when the annotated data are scarce. Thus, previous approaches tackling this problem are in…
Outline to Story: Fine-grained Controllable Story Generation from Cascaded Events
Le Fang, Tao Zeng, Chaochun Liu +3
Large-scale pretrained language models have shown thrilling generation capabilities, especially when they generate consistent long text in thousands of words with ease. However, us…
Transformer-based Conditional Variational Autoencoder for Controllable Story Generation
Le Fang, Tao Zeng, Chaochun Liu +3
We investigate large-scale latent variable models (LVMs) for neural story generation -- an under-explored application for open-domain long text -- with objectives in two threads: g…