Ning Chen

According to our database, Ning Chen authored at least 49 papers between 2008 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Improving Viewpoint Robustness for Visual Recognition via Adversarial Training.

Shouwei Ruan, Yinpeng Dong, Hang Su, Jianteng Peng, Ning Chen, Xingxing Wei

CoRR, 2023

Towards Viewpoint-Invariant Visual Recognition via Adversarial Training.

Shouwei Ruan, Yinpeng Dong, Hang Su, Jianteng Peng, Ning Chen, Xingxing Wei

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition.

Xiao Yang, Chang Liu, Longlong Xu, Yikai Wang, Yinpeng Dong, Ning Chen, Hang Su, Jun Zhu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

StackVAE-G: An efficient and interpretable model for time series anomaly detection.

Wenkai Li, Wenbo Hu, Ting Chen, Ning Chen, Cheng Feng

AI Open, January, 2022

Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk.

Chengyang Ying, Xinning Zhou, Hang Su, Dong Yan, Ning Chen, Jun Zhu

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021

kLDM: Inferring Multiple Metagenomic Association Networks Based on the Variation of Environmental Factors.

Yuqing Yang, Xin Wang, Kaikun Xie, Congmin Zhu, Ning Chen, Ting Chen

Genom. Proteom. Bioinform., 2021

redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-cell RNA-seq Data and Applications in Cancer.

Kaikun Xie, Zehua Liu, Ning Chen, Ting Chen

Genom. Proteom. Bioinform., 2021

Stacking VAE with Graph Neural Networks for Effective and Interpretable Time Series Anomaly Detection.

Wenkai Li, Wenbo Hu, Ning Chen, Cheng Feng

CoRR, 2021

Combining Tree Search and Action Prediction for State-of-the-Art Performance in DouDiZhu.

Yunsheng Zhang, Dong Yan, Bei Shi, Haobo Fu, Qiang Fu, Hang Su, Jun Zhu, Ning Chen

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting.

Qingyi Pan, Wenbo Hu, Ning Chen

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

2020

Joint Medical Ontology Representation Learning for Healthcare Predictions.

Ke Wang, Ning Chen, Ting Chen

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Automatic Emergency Diagnosis with Knowledge-Based Tree Decoding.

Ke Wang, Xuyan Chen, Ning Chen, Ting Chen

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness.

Tianyu Pang, Kun Xu, Yinpeng Dong, Chao Du, Ning Chen, Jun Zhu

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

An adaptive PNN-DS approach to classification using multi-sensor information fusion.

Ning Chen, Fuchun Sun, Linge Ding, Hongqiao Wang

Neural Comput. Appl., 2019

Ontology-based venous thromboembolism risk assessment model developing from medical records.

Yuqing Yang, Xin Wang, Yu Huang, Ning Chen, Juhong Shi, Ting Chen

BMC Medical Informatics Decis. Mak., 2019

Improving Adversarial Robustness via Promoting Ensemble Diversity.

Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu

Proceedings of the 36th International Conference on Machine Learning, 2019

Computer-aided Diagnosis of Ambulatory Electrocardiograms via ASRS: Active-Selection-Random-Selection.

Xuan Zhang, Guangyu Wang, Ning Chen, Ting Chen

Proceedings of the 2019 IEEE International Conference on Healthcare Informatics, 2019

DCMN: Double Core Memory Network for Patient Outcome Prediction with Multimodal Data.

Yujuan Feng, Zhenxing Xu, Lin Gan, Ning Chen, Bin Yu, Ting Chen, Fei Wang

Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

How Robust is Your Automatic Diagnosis Model?

Ke Wang, Guangyu Wang, Ning Chen, Ting Chen

Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019

DeepTriager: A Neural Attention Model for Emergency Triage with Electronic Health Records.

Guangyu Wang, Xiaohong Liu, Ken Xie, Ning Chen, Ting Chen

Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019

Bone Age Assessment by Deep Convolutional Neural Networks Combined with Clinical TW3-RUS.

Xiaohong Liu, Guangyu Wang, Yiming Xu, Ning Chen, Ting Chen

Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019

2018

Dropout training for SVMs with data augmentation.

Ning Chen, Jun Zhu, Jianfei Chen, Ting Chen

Frontiers Comput. Sci., 2018

Message Passing Stein Variational Gradient Descent.

Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang

Proceedings of the 35th International Conference on Machine Learning, 2018

Ontology-based Venous Thromboembolism Risk Factors Mining and Model Developing from Medical Records.

Yuqing Yang, Xin Wang, Yu Huang, Ning Chen, Juhong Shi, Ting Chen

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2018

2017

Elastic restricted Boltzmann machines for cancer data analysis.

Sai Zhang, Muxuan Liang, Zhongjun Zhou, Chen Zhang, Ning Chen, Ting Chen, Jianyang Zeng

Quant. Biol., 2017

Predicting enhancers with deep convolutional neural networks.

Xu Min, Wanwen Zeng, Shengquan Chen, Ning Chen, Ting Chen, Rui Jiang

BMC Bioinform., 2017

Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding.

Xu Min, Wanwen Zeng, Ning Chen, Ting Chen, Rui Jiang

Bioinform., 2017

DACE: a scalable DP-means algorithm for clustering extremely large sequence data.

Linhao Jiang, Yichao Dong, Ning Chen, Ting Chen

Bioinform., 2017

Patient outcome prediction via convolutional neural networks based on multi-granularity medical concept embedding.

Yujuan Feng, Xu Min, Ning Chen, Hu Chen, Xiaolei Xie, Haibo Wang, Ting Chen

Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine, 2017

Learning Attributes from the Crowdsourced Relative Labels.

Tian Tian, Ning Chen, Jun Zhu

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

mLDM: A New Hierarchical Bayesian Statistical Model for Sparse Microbial Association Discovery.

Yuqing Yang, Ning Chen, Ting Chen

Proceedings of the Research in Computational Molecular Biology - 20th Annual Conference, 2016

DeepEnhancer: Predicting enhancers by convolutional neural networks.

Xu Min, Ning Chen, Ting Chen, Rui Jiang

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2016

Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation.

Bei Chen, Ning Chen, Jun Zhu, Jiaming Song, Bo Zhang

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Discriminative Relational Topic Models.

Ning Chen, Jun Zhu, Fei Xia, Bo Zhang

IEEE Trans. Pattern Anal. Mach. Intell., 2015

2014

Learning Harmonium Models With Infinite Latent Features.

Ning Chen, Jun Zhu, Fuchun Sun, Bo Zhang

IEEE Trans. Neural Networks Learn. Syst., 2014

Bayesian inference with posterior regularization and applications to infinite latent SVMs.

Jun Zhu, Ning Chen, Eric P. Xing

J. Mach. Learn. Res., 2014

Gibbs max-margin topic models with data augmentation.

Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang

J. Mach. Learn. Res., 2014

Max-margin latent feature relational models for entity-attribute networks.

Fei Xia, Ning Chen, Jun Zhu, Aonan Zhang, Xiaoming Jin

Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

Dropout Training for Support Vector Machines.

Ning Chen, Jun Zhu, Jianfei Chen, Bo Zhang

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

Generalized Relational Topic Models with Data Augmentation.

Ning Chen, Jun Zhu, Fei Xia, Bo Zhang

Proceedings of the IJCAI 2013, 2013

Gibbs Max-Margin Topic Models with Fast Sampling Algorithms.

Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Large-Margin Predictive Latent Subspace Learning for Multiview Data Analysis.

Ning Chen, Jun Zhu, Fuchun Sun, Eric P. Xing

IEEE Trans. Pattern Anal. Mach. Intell., 2012

Bayesian Inference with Posterior Regularization and Infinite Latent Support Vector Machines

Jun Zhu, Ning Chen, Eric P. Xing

CoRR, 2012

2011

Infinite Latent SVM for Classification and Multi-task Learning.

Jun Zhu, Ning Chen, Eric P. Xing

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Conditional topical coding: an efficient topic model conditioned on rich features.

Jun Zhu, Ni Lao, Ning Chen, Eric P. Xing

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines.

Jun Zhu, Ning Chen, Eric P. Xing

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

An unbiased LSSVM model for classification and regression.

Hongqiao Wang, Fuchun Sun, Yan-Ning Cai, Linge Ding, Ning Chen

Soft Comput., 2010

Predictive Subspace Learning for Multi-view Data: a Large Margin Approach.

Ning Chen, Jun Zhu, Eric P. Xing

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2008

A Sparse Sampling Method for Classification Based on Likelihood Factor.

Linge Ding, Fuchun Sun, Hongqiao Wang, Ning Chen

Proceedings of the Advances in Neural Networks, 2008