Search Results for author: Li Pan

Found 5 papers, 3 papers with code

Identifying Autism Spectrum Disorder Based on Individual-Aware Down-Sampling and Multi-Modal Learning

1 code implementation19 Sep 2021 Li Pan, Jundong Liu, Mingqin Shi, Chi Wah Wong, Kei Hang Katie Chan

To further recalibrate the distribution of the extracted features under phenotypic information, we subsequently embed the sparse feature vectors into a population graph, where the hidden inter-subject heterogeneity and homogeneity are explicitly expressed as inter- and intra-community connectivity differences, and utilize Graph Convolutional Networks to learn the node embeddings.

Market-Oriented Online Bi-Objective Service Scheduling for Pleasingly Parallel Jobs with Variable Resources in Cloud Environments

no code implementations17 Feb 2021 Bingbing Zheng, Li Pan, Shijun Liu

In this paper, we study the market-oriented online bi-objective service scheduling problem for pleasingly parallel jobs with variable resources in cloud environments, from the perspective of SaaS (Software-as-as-Service) providers who provide job-execution services.

Distributed, Parallel, and Cluster Computing

Distinguish Confusing Law Articles for Legal Judgment Prediction

1 code implementation ACL 2020 Nuo Xu, Pinghui Wang, Long Chen, Li Pan, Xiaoyan Wang, Junzhou Zhao

Legal Judgment Prediction (LJP) is the task of automatically predicting a law case's judgment results given a text describing its facts, which has excellent prospects in judicial assistance systems and convenient services for the public.

Multimodal Deep Network Embedding with Integrated Structure and Attribute Information

no code implementations28 Mar 2019 Conghui Zheng, Li Pan, Peng Wu

Network embedding is the process of learning low-dimensional representations for nodes in a network, while preserving node features.

Network Embedding

ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding

1 code implementation CVPR 2019 Ning Liu, Yongchao Long, Changqing Zou, Qun Niu, Li Pan, Hefeng Wu

We propose an attention-injective deformable convolutional network called ADCrowdNet for crowd understanding that can address the accuracy degradation problem of highly congested noisy scenes.

Crowd Counting

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