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Cheng Ling
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2020 – today
- 2024
- [c20]Nian Li, Xin Ban, Cheng Ling, Chen Gao, Lantao Hu, Peng Jiang, Kun Gai, Yong Li, Qingmin Liao:
Modeling User Fatigue for Sequential Recommendation. SIGIR 2024: 996-1005 - [i3]Bowen Zheng, Zihan Lin, Enze Liu, Chen Yang, Enyang Bai, Cheng Ling, Wayne Xin Zhao, Ji-Rong Wen:
A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation. CoRR abs/2403.13574 (2024) - [i2]Nian Li, Xin Ban, Cheng Ling, Chen Gao, Lantao Hu, Peng Jiang, Kun Gai, Yong Li, Qingmin Liao:
Modeling User Fatigue for Sequential Recommendation. CoRR abs/2405.11764 (2024) - [i1]Enze Liu, Bowen Zheng, Cheng Ling, Lantao Hu, Han Li, Wayne Xin Zhao:
End-to-End Learnable Item Tokenization for Generative Recommendation. CoRR abs/2409.05546 (2024) - 2022
- [c19]Ruobing Xie, Cheng Ling, Shaoliang Zhang, Feng Xia, Leyu Lin:
Multi-granularity Fatigue in Recommendation. CIKM 2022: 4595-4599 - 2021
- [j9]Ruofei Bai, Cheng Ling, Lei Cai, Jingyang Gao:
Cnngeno: A high-precision deep learning based strategy for the calling of structural variation genotype. Comput. Biol. Chem. 94: 107417 (2021) - 2020
- [j8]Guangren Cai, Gang Lu, Junxia Guo, Cheng Ling, Ruiqi Li:
Fast Representative Sampling in Large-Scale Online Social Networks. IEEE Access 8: 77106-77119 (2020) - [j7]Cheng Ling, Wenhao Cheng, Haoyu Zhang, Hanhao Zhu, Hua Zhang:
Deep Neighbor Information Learning From Evolution Trees for Phylogenetic Likelihood Estimates. IEEE Access 8: 220692-220702 (2020) - [c18]Cheng Ling, Yitian Shen, Jingyang Gao:
A novel synonymous processing method based on amino acid substitution matrics for the classification of G-protein-coupled receptors. BIBM 2020: 451-454 - [c17]Mingyan Li, Cheng Ling:
The Likelihood Prediction of Phylogenetic Trees based on Artificial Neural Network: a new perspective and preliminary attempt. BIBM 2020: 2977-2979 - [c16]Ruobing Xie, Cheng Ling, Yalong Wang, Rui Wang, Feng Xia, Leyu Lin:
Deep Feedback Network for Recommendation. IJCAI 2020: 2519-2525
2010 – 2019
- 2019
- [c15]Ruofei Bai, Liwei Gao, Cheng Ling, Jingyang Gao:
CnnSV-Typer: Calling of Structural Variation Genotype Based on CUDA Acceleration. HPCC/SmartCity/DSS 2019: 2461-2466 - 2018
- [j6]Jing Wang, Jing-Yang Gao, Cheng Ling:
Deletion genotype calling on the basis of sequence visualisation and image classification. Int. J. Data Min. Bioinform. 20(2): 109-122 (2018) - [j5]Xiurui Geng, Weitun Yang, Luyan Ji, Cheng Ling, Suixin Yang:
A Piecewise Linear Strategy of Target Detection for Multispectral/Hyperspectral Image. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 11(3): 951-961 (2018) - [c14]Cheng Ling, Lin Yue, Jing-Yang Gao:
Classification of G-protein Coupled Receptors Based on Semi-navïe Bayesian Inference. BIBM 2018: 2262-2268 - [c13]Miaosen Ding, Jing-Yang Gao, Cheng Ling, Liwei Gao:
cnnCNV: A Sensitive and Efficient Method for Detecting Copy Number Variation based on Convolutional Neural Networks. BIBM 2018: 2744-2746 - [c12]Yichan Li, Jing-Yang Gao, Cheng Ling, Haoyu Zhang:
GPU Parallelism of Phylogenetic Likelihood Estimates for Protein Data. HPCC/SmartCity/DSS 2018: 632-639 - 2017
- [j4]Xiaodong Zhang, Cheng Ling, Jingyang Gao:
基于特征挖掘的基因组缺失变异集成检测方法 (Integrated Feature Mining Based Approach for Calling Genomic Deletions). 计算机科学 44(1): 80-83 (2017) - [c11]Man Li, Cheng Ling, Jing-Yang Gao:
An efficient CNN-based classification on G-protein Coupled Receptors using TF-IDF and N-gram. ISCC 2017: 924-931 - 2016
- [j3]Cheng Ling, Tsuyoshi Hamada, Jing-Yang Gao, Guoguang Zhao, Donghong Sun, Weifeng Shi:
MrBayes tgMC3++: A High Performance and Resource-Efficient GPU-Oriented Phylogenetic Analysis Method. IEEE ACM Trans. Comput. Biol. Bioinform. 13(5): 845-854 (2016) - [c10]Jing Wang, Cheng Ling, Jing-Yang Gao:
A high-precision shallow Convolutional Neural Network based strategy for the detection of Genomic Deletions. BIBM 2016: 1806-1813 - [c9]Cheng Ling, Arong Luo, Jing-Yang Gao:
MrBayes 3.2.6 on Tianhe-1A: A High Performance and Distributed Implementation of Phylogenetic Analysis. ICPADS 2016: 1181-1186 - [c8]Cheng Ling, Jing-Yang Gao, Gang Lu:
Phylogenetic Likelihood Estimation on GPUs Using Vertical Partitioning Scheme. Trustcom/BigDataSE/ISPA 2016: 1210-1217 - 2015
- [c7]Cheng Ling, Chunbao Zhou, Arong Luo, Guoguang Zhao, Tsuyoshi Hamada, Xiaoyan Zhu:
Optimizing the Bayesian Inference of Phylogeny on Graphic Processors. CCGRID 2015: 333-342 - [c6]Guoguang Zhao, Cheng Ling, Donghong Sun:
SparkSW: Scalable Distributed Computing System for Large-Scale Biological Sequence Alignment. CCGRID 2015: 845-852 - 2012
- [j2]Khaled Benkrid, Ali Akoglu, Cheng Ling, Yang Song, Ying Liu, Xiang Tian:
High Performance Biological Pairwise Sequence Alignment: FPGA versus GPU versus Cell BE versus GPP. Int. J. Reconfigurable Comput. 2012: 752910:1-752910:15 (2012) - [j1]Cheng Ling, Khaled Benkrid, Tsuyoshi Hamada:
High performance phylogenetic analysis on CUDA-compatible GPUs. SIGARCH Comput. Archit. News 40(5): 52-57 (2012) - 2011
- [c5]Mohd Nazrin Md. Isa, Khaled Benkrid, Thomas Clayton, Cheng Ling, Ahmet T. Erdogan:
An FPGA-based parameterised and scalable optimal solutions for pairwise biological sequence analysis. AHS 2011: 344-351 - [c4]Cheng Ling, Khaled Benkrid, Ahmet T. Erdogan:
High performance Intra-task parallelization of Multiple Sequence Alignments on CUDA-compatible GPUs. AHS 2011: 360-366 - 2010
- [c3]Keisuke Dohi, Khaled Benkrid, Cheng Ling, Tsuyoshi Hamada, Yuichiro Shibata:
Highly efficient mapping of the Smith-Waterman algorithm on CUDA-compatible GPUs. ASAP 2010: 29-36 - [c2]Cheng Ling, Khaled Benkrid:
Design and implementation of a CUDA-compatible GPU-based core for gapped BLAST algorithm. ICCS 2010: 495-504
2000 – 2009
- 2009
- [c1]Cheng Ling, Khaled Benkrid, Tsuyoshi Hamada:
A parameterisable and scalable Smith-Waterman algorithm implementation on CUDA-compatible GPUs. SASP 2009: 94-100
Coauthor Index
aka: Jing-Yang Gao
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