John W. Paisley

According to our database, John W. Paisley authored at least 104 papers between 2007 and 2023.

Collaborative distances:

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

2023

Learning Rate Dropout.

Huangxing Lin, Weihong Zeng, Yihong Zhuang, Xinghao Ding, Yue Huang, John W. Paisley

IEEE Trans. Neural Networks Learn. Syst., November, 2023

Double Normalizing Flows: Flexible Bayesian Gaussian Process ODEs Learning.

Jian Xu, Shian Du, Junmei Yang, Xinghao Ding, John W. Paisley, Delu Zeng

CoRR, 2023

Nonlinear Kalman Filtering with Reparametrization Gradients.

San Gultekin, Brendan Kitts, Aaron Flores, John W. Paisley

CoRR, 2023

Self-Supervised Image Denoising Using Implicit Deep Denoiser Prior.

Huangxing Lin, Yihong Zhuang, Xinghao Ding, Delu Zeng, Yue Huang, Xiaotong Tu, John W. Paisley

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Few-shot medical image segmentation using a global correlation network with discriminative embedding.

Liyan Sun, Chenxin Li, Xinghao Ding, Yue Huang, Zhong Chen, Guisheng Wang, Yizhou Yu, John W. Paisley

Comput. Biol. Medicine, 2022

Bayesian Nonparametric Model Averaging Using Scalable Gaussian Process Representations.

John W. Paisley, Sebastian Rowland, Jeremiah Zhe Liu, Brent A. Coull, Marianthi-Anna Kioumourtzoglou

Proceedings of the IEEE International Conference on Big Data, 2022

Probabilistic Orthogonal Matching Pursuit.

Ghazal Fazelnia, John W. Paisley

Proceedings of the IEEE International Conference on Big Data, 2022

2021

Deep Multiscale Detail Networks for Multiband Spectral Image Sharpening.

Xueyang Fu, Wu Wang, Yue Huang, Xinghao Ding, John W. Paisley

IEEE Trans. Neural Networks Learn. Syst., 2021

Successive Graph Convolutional Network for Image De-raining.

Xueyang Fu, Qi Qi, Zheng-Jun Zha, Xinghao Ding, Feng Wu, John W. Paisley

Int. J. Comput. Vis., 2021

Self-Verification in Image Denoising.

Huangxing Lin, Yihong Zhuang, Delu Zeng, Yue Huang, Xinghao Ding, John W. Paisley

CoRR, 2021

2020

MBA: Mini-Batch AUC Optimization.

San Gultekin, Avishek Saha, Adwait Ratnaparkhi, John W. Paisley

IEEE Trans. Neural Networks Learn. Syst., 2020

Lightweight Pyramid Networks for Image Deraining.

Xueyang Fu, Borong Liang, Yue Huang, Xinghao Ding, John W. Paisley

IEEE Trans. Neural Networks Learn. Syst., 2020

A 3D Spatially Weighted Network for Segmentation of Brain Tissue From MRI.

Liyan Sun, Wenao Ma, Xinghao Ding, Yue Huang, Dong Liang, John W. Paisley

IEEE Trans. Medical Imaging, 2020

An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection.

Liyan Sun, Jiexiang Wang, Yue Huang, Xinghao Ding, Hayit Greenspan, John W. Paisley

IEEE J. Biomed. Health Informatics, 2020

Rain O'er Me: Synthesizing Real Rain to Derain With Data Distillation.

Huangxing Lin, Yanlong Li, Xueyang Fu, Xinghao Ding, Yue Huang, John W. Paisley

IEEE Trans. Image Process., 2020

A dual-domain deep lattice network for rapid MRI reconstruction.

Liyan Sun, Yawen Wu, Binglin Shu, Xinghao Ding, Congbo Cai, Yue Huang, John W. Paisley

Neurocomputing, 2020

Adaptive noise imitation for image denoising.

Huangxing Lin, Yihong Zhuang, Yue Huang, Xinghao Ding, Yizhou Yu, Xiaoqing Liu, John W. Paisley

CoRR, 2020

Bayesian recurrent state space model for rs-fMRI.

Arunesh Mittal, Scott W. Linderman, John W. Paisley, Paul Sajda

CoRR, 2020

Deep Bayesian Nonparametric Factor Analysis.

Arunesh Mittal, Paul Sajda, John W. Paisley

CoRR, 2020

Risk Bounds for Low Cost Bipartite Ranking.

San Gultekin, John W. Paisley

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Mixed membership recurrent neural networks for modeling customer purchases.

Ghazal Fazelnia, Mark Ibrahim, Ceena Modarres, Kevin Wu, John W. Paisley

Proceedings of the ICAIF '20: The First ACM International Conference on AI in Finance, 2020

2019

Online Forecasting Matrix Factorization.

San Gultekin, John W. Paisley

IEEE Trans. Signal Process., 2019

A Deep Information Sharing Network for Multi-Contrast Compressed Sensing MRI Reconstruction.

Liyan Sun, Zhiwen Fan, Xueyang Fu, Yue Huang, Xinghao Ding, John William Paisley

IEEE Trans. Image Process., 2019

Noise2Blur: Online Noise Extraction and Denoising.

Huangxing Lin, Weihong Zeng, Xinghao Ding, Xueyang Fu, Yue Huang, John W. Paisley

CoRR, 2019

Learning Rate Dropout.

Huangxing Lin, Weihong Zeng, Xinghao Ding, Yue Huang, Chenxi Huang, John W. Paisley

CoRR, 2019

Reweighted Expectation Maximization.

Adji B. Dieng, John W. Paisley

CoRR, 2019

Rain O'er Me: Synthesizing real rain to derain with data distillation.

Huangxing Lin, Yanlong Li, Xinghao Ding, Weihong Zeng, Yue Huang, John W. Paisley

CoRR, 2019

A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI.

Tao Tu, John W. Paisley, Stefan Haufe, Paul Sajda

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Accurate Uncertainty Estimation and Decomposition in Ensemble Learning.

Jeremiah Z. Liu, John W. Paisley, Marianthi-Anna Kioumourtzoglou, Brent A. Coull

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network.

Liyan Sun, Zhiwen Fan, Xinghao Ding, Yue Huang, John W. Paisley

Proceedings of the Information Processing in Medical Imaging, 2019

Random Function Priors for Correlation Modeling.

Aonan Zhang, John W. Paisley

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

Deep Blind Hyperspectral Image Fusion.

Wu Wang, Weihong Zeng, Yue Huang, Xinghao Ding, John W. Paisley

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

JPEG Artifacts Reduction via Deep Convolutional Sparse Coding.

Xueyang Fu, Zheng-Jun Zha, Feng Wu, Xinghao Ding, John W. Paisley

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Fully Supervised Speaker Diarization.

Aonan Zhang, Quan Wang, Zhenyao Zhu, John W. Paisley, Chong Wang

Proceedings of the IEEE International Conference on Acoustics, 2019

Global Explanations of Neural Networks: Mapping the Landscape of Predictions.

Mark Ibrahim, Melissa Louie, Ceena Modarres, John W. Paisley

Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018

Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network.

Xiangyong Cao, Feng Zhou, Lin Xu, Deyu Meng, Zongben Xu, John W. Paisley

IEEE Trans. Image Process., 2018

A Modified EM Algorithm for ISAR Scatterer Trajectory Matrix Completion.

Lei Liu, Feng Zhou, Xueru Bai, John W. Paisley, Hongbing Ji

IEEE Trans. Geosci. Remote. Sens., 2018

Mixed Membership Recurrent Neural Networks.

Ghazal Fazelnia, Mark Ibrahim, Ceena Modarres, Kevin Wu, John W. Paisley

CoRR, 2018

Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process.

Jeremiah Zhe Liu, John W. Paisley, Marianthi-Anna Kioumourtzoglou, Brent A. Coull

CoRR, 2018

A Deep Tree-Structured Fusion Model for Single Image Deraining.

Xueyang Fu, Qi Qi, Yue Huang, Xinghao Ding, Feng Wu, John W. Paisley

CoRR, 2018

Towards Explainable Deep Learning for Credit Lending: A Case Study.

Ceena Modarres, Mark Ibrahim, Melissa Louie, John W. Paisley

CoRR, 2018

An Adversarial Learning Approach to Medical Image Synthesis for Lesion Removal.

Liyan Sun, Jiexiang Wang, Xinghao Ding, Yue Huang, John W. Paisley

CoRR, 2018

A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction.

Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John W. Paisley

CoRR, 2018

A Divide-and-Conquer Approach to Compressed Sensing MRI.

Liyan Sun, Zhiwen Fan, Xinghao Ding, Congbo Cai, Yue Huang, John W. Paisley

CoRR, 2018

A Deep Error Correction Network for Compressed Sensing MRI.

Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John W. Paisley

CoRR, 2018

MEnet: A Metric Expression Network for Salient Object Segmentation.

Shulian Cai, Jiabin Huang, Delu Zeng, Xinghao Ding, John W. Paisley

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Deep Bayesian Nonparametric Tracking.

Aonan Zhang, John W. Paisley

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

CRVI: Convex Relaxation for Variational Inference.

Ghazal Fazelnia, John W. Paisley

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

Asymptotic Simulated Annealing for Variational Inference.

San Gultekin, Aonan Zhang, John W. Paisley

Proceedings of the IEEE Global Communications Conference, 2018

A Segmentation-Aware Deep Fusion Network for Compressed Sensing MRI.

Zhiwen Fan, Liyan Sun, Xinghao Ding, Yue Huang, Congbo Cai, John W. Paisley

Proceedings of the Computer Vision - ECCV 2018, 2018

Compressed Sensing MRI Using a Recursive Dilated Network.

Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John W. Paisley

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Nonlinear Kalman Filtering With Divergence Minimization.

San Gultekin, John W. Paisley

IEEE Trans. Signal Process., 2017

Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal.

Xueyang Fu, Jiabin Huang, Xinghao Ding, Yinghao Liao, John W. Paisley

IEEE Trans. Image Process., 2017

Hyperspectral Image Segmentation with Markov Random Fields and a Convolutional Neural Network.

Xiangyong Cao, Feng Zhou, Lin Xu, Deyu Meng, Zongben Xu, John W. Paisley

CoRR, 2017

Revealing common disease mechanisms shared by tumors of different tissues of origin through semantic representation of genomic alterations and topic modeling.

Vicky Chen, John W. Paisley, Xinghua Lu

BMC Genom., 2017

Variational Inference via \chi Upper Bound Minimization.

Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Location Dependent Dirichlet Processes.

Shiliang Sun, John W. Paisley, Qiuyang Liu

Proceedings of the Intelligence Science and Big Data Engineering, 2017

TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency.

Adji B. Dieng, Chong Wang, Jianfeng Gao, John W. Paisley

Proceedings of the 5th International Conference on Learning Representations, 2017

PanNet: A Deep Network Architecture for Pan-Sharpening.

Junfeng Yang, Xueyang Fu, Yuwen Hu, Yue Huang, Xinghao Ding, John W. Paisley

Proceedings of the IEEE International Conference on Computer Vision, 2017

Removing Rain from Single Images via a Deep Detail Network.

Xueyang Fu, Jiabin Huang, Delu Zeng, Yue Huang, Xinghao Ding, John W. Paisley

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016

A fusion-based enhancing method for weakly illuminated images.

Xueyang Fu, Delu Zeng, Yue Huang, Yinghao Liao, Xinghao Ding, John W. Paisley

Signal Process., 2016

The $χ$-Divergence for Approximate Inference.

Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei

CoRR, 2016

Markov Latent Feature Models.

Aonan Zhang, John W. Paisley

Proceedings of the 33nd International Conference on Machine Learning, 2016

Stochastic Variational Inference for the HDP-HMM.

Aonan Zhang, San Gultekin, John W. Paisley

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Nested Hierarchical Dirichlet Processes.

John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan

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

Combinatorial Clustering and the Beta Negative Binomial Process.

Tamara Broderick, Lester W. Mackey, John W. Paisley, Michael I. Jordan

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

Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts.

Aaron Schein, John W. Paisley, David M. Blei, Hanna M. Wallach

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Markov Mixed Membership Models.

Aonan Zhang, John W. Paisley

Proceedings of the 32nd International Conference on Machine Learning, 2015

Landmarking Manifolds with Gaussian Processes.

Dawen Liang, John W. Paisley

Proceedings of the 32nd International Conference on Machine Learning, 2015

Pan-Sharpening with a Hyper-Laplacian Penalty.

Yiyong Jiang, Xinghao Ding, Delu Zeng, Yue Huang, John W. Paisley

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

Scalable Bayesian nonparametric dictionary learning.

Sarper Sertoglu, John W. Paisley

Proceedings of the 23rd European Signal Processing Conference, 2015

2014

Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference.

John W. Paisley, David M. Blei, Michael I. Jordan

Proceedings of the Handbook of Mixed Membership Models and Their Applications., 2014

Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI.

Yue Huang, John W. Paisley, Qin Lin, Xinghao Ding, Xueyang Fu, Xiao-Ping (Steven) Zhang

IEEE Trans. Image Process., 2014

Codebook-based Scalable Music Tagging with Poisson Matrix Factorization.

Dawen Liang, John W. Paisley, Dan Ellis

Proceedings of the 15th International Society for Music Information Retrieval Conference, 2014

A Collaborative Kalman Filter for Time-Evolving Dyadic Processes.

San Gultekin, John W. Paisley

Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

Pan-sharpening with a Bayesian nonparametric dictionary learning model.

Xinghao Ding, Yiyong Jiang, Yue Huang, John W. Paisley

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Stochastic variational inference.

Matthew D. Hoffman, David M. Blei, Chong Wang, John W. Paisley

J. Mach. Learn. Res., 2013

MR Image Reconstruction from Undersampled k-Space with Bayesian Dictionary Learning

Yue Huang, John W. Paisley, Xianbo Chen, Xinghao Ding, Feng Huang, Xiao-Ping (Steven) Zhang

CoRR, 2013

A Nested HDP for Hierarchical Topic Models

John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan

Proceedings of the 1st International Conference on Learning Representations, 2013

Pan-sharpening based on nonparametric Bayesian adaptive dictionary learning.

Jin Xie, Yue Huang, John W. Paisley, Xinghao Ding, Xiao-Ping (Steven) Zhang

Proceedings of the IEEE International Conference on Image Processing, 2013

Compressed sensing MRI with Bayesian dictionary learning.

Xinghao Ding, John W. Paisley, Yue Huang, Xianbo Chen, Feng Huang, Xiao-Ping (Steven) Zhang

Proceedings of the IEEE International Conference on Image Processing, 2013

2012

Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images.

Mingyuan Zhou, Haojun Chen, John W. Paisley, Lu Ren, Lingbo Li, Zhengming Xing, David B. Dunson, Guillermo Sapiro, Lawrence Carin

IEEE Trans. Image Process., 2012

Stick-Breaking Beta Processes and the Poisson Process.

John W. Paisley, David M. Blei, Michael I. Jordan

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Variational Bayesian Inference with Stochastic Search.

John W. Paisley, David M. Blei, Michael I. Jordan

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Corrections to "Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds".

Minhua Chen, Jorge G. Silva, John W. Paisley, Chunping Wang, David B. Dunson, Lawrence Carin

IEEE Trans. Signal Process., 2011

Online Variational Inference for the Hierarchical Dirichlet Process.

Chong Wang, John W. Paisley, David M. Blei

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling.

John W. Paisley, Chong Wang, David M. Blei

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Variational Inference for Stick-Breaking Beta Process Priors.

John W. Paisley, Lawrence Carin, David M. Blei

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

2010

Machine Learning with Dirichlet and Beta Process Priors: Theory and Applications.

John William Paisley

PhD thesis, 2010

Active learning and basis selection for kernel-based linear models: a Bayesian perspective.

John W. Paisley, Xuejun Liao, Lawrence Carin

IEEE Trans. Signal Process., 2010

Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds.

Minhua Chen, Jorge G. Silva, John W. Paisley, Chunping Wang, David B. Dunson, Lawrence Carin

IEEE Trans. Signal Process., 2010

Hierarchical Bayesian Modeling of Topics in Time-Stamped Documents.

Iulian Pruteanu-Malinici, Lu Ren, John W. Paisley, Eric Wang, Lawrence Carin

IEEE Trans. Pattern Anal. Mach. Intell., 2010

Bayesian Inference of the Number of Factors in Gene-Expression Analysis: Application to Human Virus Challenge Studies.

Bo Chen, Minhua Chen, John W. Paisley, Aimee K. Zaas, Christopher W. Woods, Geoffrey S. Ginsburg, Alfred O. Hero III, Joseph E. Lucas, David B. Dunson, Lawrence Carin

BMC Bioinform., 2010

A Stick-Breaking Construction of the Beta Process.

John W. Paisley, Aimee K. Zaas, Christopher W. Woods, Geoffrey S. Ginsburg, Lawrence Carin

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Nonparametric image interpolation and dictionary learning using spatially-dependent Dirichlet and beta process priors.

John W. Paisley, Mingyuan Zhou, Guillermo Sapiro, Lawrence Carin

Proceedings of the International Conference on Image Processing, 2010

A nonparametric Bayesian model for kernel matrix completion.

John W. Paisley, Lawrence Carin

Proceedings of the IEEE International Conference on Acoustics, 2010

Sparse linear regression with beta process priors.

Bo Chen, John W. Paisley, Lawrence Carin

Proceedings of the IEEE International Conference on Acoustics, 2010

2009

Hidden Markov models with stick-breaking priors.

John W. Paisley, Lawrence Carin

IEEE Trans. Signal Process., 2009

Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations.

Mingyuan Zhou, Haojun Chen, John W. Paisley, Lu Ren, Guillermo Sapiro, Lawrence Carin

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Nonparametric factor analysis with beta process priors.

John W. Paisley, Lawrence Carin

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Dirichlet process mixture models with multiple modalities.

John W. Paisley, Lawrence Carin

Proceedings of the IEEE International Conference on Acoustics, 2009

2008

Multi-Task Learning for Analyzing and Sorting Large Databases of Sequential Data.

Kai Ni, John W. Paisley, Lawrence Carin, David B. Dunson

IEEE Trans. Signal Process., 2008

2007

Music Analysis Using Hidden Markov Mixture Models.

Yuting Qi, John William Paisley, Lawrence Carin

IEEE Trans. Signal Process., 2007

Dirichlet Process HMM Mixture Models with Application to Music Analysis.

Yuting Qi, John William Paisley, Lawrence Carin

Proceedings of the IEEE International Conference on Acoustics, 2007