1 code implementation • 9 Apr 2024 • Yupei Zhang, Li Pan, Qiushi Yang, Tan Li, Zhen Chen
Specifically, to enhance the representation abilities of vision and language encoders, we propose the Multi-level Reconstruction Pre-training (MR-Pretrain) strategy, including a feature-level and data-level reconstruction, which guides models to capture the semantic information from masked inputs of different modalities.
no code implementations • 21 Mar 2024 • Bingchen Liu, Huang Peng, Weixin Zeng, Xiang Zhao, Shijun Liu, Li Pan
MulCanon unifies the learning objectives of these sub-tasks, and adopts a two-stage multi-task learning paradigm for training.
no code implementations • 6 Dec 2023 • Haoyue Wang, Li Pan, Bo Yang, Junqiang Jiang, Wenbin Li
In order to improve the accuracy of the identification of essential proteins, researchers attempted to obtain a refined PIN by combining multiple biological information to filter out some unreliable interactions in the PIN.
no code implementations • 25 Oct 2023 • Bingchen Liu, Shihao Hou, Weixin Zeng, Xiang Zhao, Shijun Liu, Li Pan
MulCanon unifies the learning objectives of diffusion model, KGE and clustering algorithms, and adopts a two-step multi-task learning paradigm for training.
no code implementations • 23 Oct 2023 • Xiaomin Ouyang, Xian Shuai, Yang Li, Li Pan, Xifan Zhang, Heming Fu, Sitong Cheng, Xinyan Wang, Shihua Cao, Jiang Xin, Hazel Mok, Zhenyu Yan, Doris Sau Fung Yu, Timothy Kwok, Guoliang Xing
ADMarker features a novel three-stage multi-modal federated learning architecture that can accurately detect digital biomarkers in a privacy-preserving manner.
1 code implementation • 24 Sep 2023 • Zheng Wang, Hongming Ding, Li Pan, Jianhua Li, Zhiguo Gong, Philip S. Yu
Graph-based semi-supervised learning (GSSL) has long been a hot research topic.
no code implementations • 23 May 2023 • Li Pan, Lv Peizhuo, Chen Kai, Cai Yuling, Xiang Fan, Zhang Shengzhi
Compared to traditional neural networks with a single exit, a multi-exit network has multiple exits that allow for early output from intermediate layers of the model, thus bringing significant improvement in computational efficiency while maintaining similar recognition accuracy.
1 code implementation • 19 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.
no code implementations • 17 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
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.
no code implementations • 28 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.
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.
Ranked #2 on Crowd Counting on TRANCOS