Dirichlet Process HMM Mixture Models with Application to Music Analysis. - Yuting Qi; John William Paisley; Lawrence Carin (ICASSP (2), 2007)

Music Analysis Using Hidden Markov Mixture Models. - Yuting Qi; John William Paisley; Lawrence Carin (IEEE Trans. Signal Process., 2007)

Multi-Task Learning for Analyzing and Sorting Large Databases of Sequential Data. - Kai Ni 007; John W. Paisley; Lawrence Carin; David B. Dunson (IEEE Trans. Signal Process., 2008)

Dirichlet process mixture models with multiple modalities. - John W. Paisley; Lawrence Carin (ICASSP, 2009)

Nonparametric factor analysis with beta process priors. - John W. Paisley; Lawrence Carin (ICML, 2009)

Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations. - Mingyuan Zhou; Haojun Chen; John W. Paisley; Lu Ren; Guillermo Sapiro; Lawrence Carin (NIPS, 2009)

Hidden Markov models with stick-breaking priors. - John W. Paisley; Lawrence Carin (IEEE Trans. Signal Process., 2009)

Sparse linear regression with beta process priors. - Bo Chen 001; John W. Paisley; Lawrence Carin (ICASSP, 2010)

A nonparametric Bayesian model for kernel matrix completion. - John W. Paisley; Lawrence Carin (ICASSP, 2010)

Nonparametric image interpolation and dictionary learning using spatially-dependent Dirichlet and beta process priors. - John W. Paisley; Mingyuan Zhou; Guillermo Sapiro; Lawrence Carin (ICIP, 2010)

A Stick-Breaking Construction of the Beta Process. - John W. Paisley; Aimee K. Zaas; Christopher W. Woods; Geoffrey S. Ginsburg; Lawrence Carin (ICML, 2010)

Bayesian Inference of the Number of Factors in Gene-Expression Analysis: Application to Human Virus Challenge Studies. - Bo Chen 001; 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)

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)

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 001; David B. Dunson; Lawrence Carin (IEEE Trans. Signal Process., 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)

Machine Learning with Dirichlet and Beta Process Priors: Theory and Applications. - John William Paisley (2010)

Variational Inference for Stick-Breaking Beta Process Priors. - John W. Paisley; Lawrence Carin; David M. Blei (ICML, 2011)

The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling. - John W. Paisley; Chong Wang 002; David M. Blei (AISTATS, 2011)

Online Variational Inference for the Hierarchical Dirichlet Process. - Chong Wang 002; John W. Paisley; David M. Blei (AISTATS, 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 001; David B. Dunson; Lawrence Carin (IEEE Trans. Signal Process., 2011)

Variational Bayesian Inference with Stochastic Search. - John W. Paisley; David M. Blei; Michael I. Jordan (ICML, 2012)

Stick-Breaking Beta Processes and the Poisson Process. - John W. Paisley; David M. Blei; Michael I. Jordan (AISTATS, 2012)

Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images. - Mingyuan Zhou; Haojun Chen; John W. Paisley; Lu Ren; Lingbo Li 002; Zhengming Xing; David B. Dunson; Guillermo Sapiro; Lawrence Carin (IEEE Trans. Image Process., 2012)

Compressed sensing MRI with Bayesian dictionary learning. - Xinghao Ding; John W. Paisley; Yue Huang 001; Xianbo Chen; Feng Huang; Xiao-Ping (Steven) Zhang (ICIP, 2013)

Pan-sharpening based on nonparametric Bayesian adaptive dictionary learning. - Jin Xie; Yue Huang 001; John W. Paisley; Xinghao Ding; Xiao-Ping (Steven) Zhang (ICIP, 2013)

A Nested HDP for Hierarchical Topic Models - John W. Paisley; Chong Wang 002; David M. Blei; Michael I. Jordan (ICLR (Workshop), 2013)

MR Image Reconstruction from Undersampled k-Space with Bayesian Dictionary Learning - Yue Huang 001; John W. Paisley; Xianbo Chen; Xinghao Ding; Feng Huang; Xiao-Ping (Steven) Zhang (CoRR, 2013)

Stochastic variational inference. - Matthew D. Hoffman; David M. Blei; Chong Wang 002; John W. Paisley (J. Mach. Learn. Res., 2013)

Pan-sharpening with a Bayesian nonparametric dictionary learning model. - Xinghao Ding; Yiyong Jiang; Yue Huang 001; John W. Paisley (AISTATS, 2014)

A Collaborative Kalman Filter for Time-Evolving Dyadic Processes. - San Gultekin; John W. Paisley (ICDM, 2014)

Codebook-based Scalable Music Tagging with Poisson Matrix Factorization. - Dawen Liang; John W. Paisley; Dan Ellis (ISMIR, 2014)

Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI. - Yue Huang 001; John W. Paisley; Qin Lin; Xinghao Ding; Xueyang Fu; Xiao-Ping (Steven) Zhang (IEEE Trans. Image Process., 2014)

Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference. - John W. Paisley; David M. Blei; Michael I. Jordan (Handbook of Mixed Membership Models and Their Applications, 2014)

Scalable Bayesian nonparametric dictionary learning. - Sarper Sertoglu; John W. Paisley (EUSIPCO, 2015)

Pan-Sharpening with a Hyper-Laplacian Penalty. - Yiyong Jiang; Xinghao Ding; Delu Zeng; Yue Huang 001; John W. Paisley (ICCV, 2015)

Landmarking Manifolds with Gaussian Processes. - Dawen Liang; John W. Paisley (ICML, 2015)

Markov Mixed Membership Models. - Aonan Zhang; John W. Paisley (ICML, 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 (KDD, 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)

Nested Hierarchical Dirichlet Processes. - John W. Paisley; Chong Wang 002; David M. Blei; Michael I. Jordan (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Stochastic Variational Inference for the HDP-HMM. - Aonan Zhang; San Gultekin; John W. Paisley (AISTATS, 2016)

Markov Latent Feature Models. - Aonan Zhang; John W. Paisley (ICML, 2016)

The $χ$-Divergence for Approximate Inference. - Adji B. Dieng; Dustin Tran; Rajesh Ranganath; John W. Paisley; David M. Blei (CoRR, 2016)

A fusion-based enhancing method for weakly illuminated images. - Xueyang Fu; Delu Zeng; Yue Huang 001; Yinghao Liao; Xinghao Ding; John W. Paisley (Signal Process., 2016)

Removing Rain from Single Images via a Deep Detail Network. - Xueyang Fu; Jiabin Huang 003; Delu Zeng; Yue Huang 001; Xinghao Ding; John W. Paisley (CVPR, 2017)

PanNet: A Deep Network Architecture for Pan-Sharpening. - Junfeng Yang; Xueyang Fu; Yuwen Hu; Yue Huang 001; Xinghao Ding; John W. Paisley (ICCV, 2017)

TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency. - Adji B. Dieng; Chong Wang 002; Jianfeng Gao 001; John W. Paisley (ICLR (Poster), 2017)

Location Dependent Dirichlet Processes. - Shiliang Sun; John W. Paisley; Qiuyang Liu (IScIDE, 2017)

Variational Inference via chi Upper Bound Minimization. - Adji Bousso Dieng; Dustin Tran; Rajesh Ranganath; John W. Paisley; David M. Blei (NIPS, 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)

Hyperspectral Image Segmentation with Markov Random Fields and a Convolutional Neural Network. - Xiangyong Cao; Feng Zhou 001; Lin Xu 001; Deyu Meng; Zongben Xu; John W. Paisley (CoRR, 2017)

Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal. - Xueyang Fu; Jiabin Huang 003; Xinghao Ding; Yinghao Liao; John W. Paisley (IEEE Trans. Image Process., 2017)

Nonlinear Kalman Filtering With Divergence Minimization. - San Gultekin; John W. Paisley (IEEE Trans. Signal Process., 2017)

Compressed Sensing MRI Using a Recursive Dilated Network. - Liyan Sun; Zhiwen Fan; Yue Huang 001; Xinghao Ding; John W. Paisley (AAAI, 2018)

A Segmentation-Aware Deep Fusion Network for Compressed Sensing MRI. - Zhiwen Fan; Liyan Sun; Xinghao Ding; Yue Huang 001; Congbo Cai; John W. Paisley (ECCV (6), 2018)

Asymptotic Simulated Annealing for Variational Inference. - San Gultekin; Aonan Zhang; John W. Paisley (GLOBECOM, 2018)

CRVI: Convex Relaxation for Variational Inference. - Ghazal Fazelnia; John W. Paisley (ICML, 2018)

Deep Bayesian Nonparametric Tracking. - Aonan Zhang; John W. Paisley (ICML, 2018)

MEnet: A Metric Expression Network for Salient Object Segmentation. - Shulian Cai; Jiabin Huang 003; Delu Zeng; Xinghao Ding; John W. Paisley (IJCAI, 2018)

A Deep Error Correction Network for Compressed Sensing MRI. - Liyan Sun; Zhiwen Fan; Yue Huang 001; 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 001; John W. Paisley (CoRR, 2018)

A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction. - Liyan Sun; Zhiwen Fan; Yue Huang 001; Xinghao Ding; John W. Paisley (CoRR, 2018)

An Adversarial Learning Approach to Medical Image Synthesis for Lesion Removal. - Liyan Sun; Jiexiang Wang; Xinghao Ding; Yue Huang 001; 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)

A Deep Tree-Structured Fusion Model for Single Image Deraining. - Xueyang Fu; Qi Qi 005; Yue Huang 001; Xinghao Ding; Feng Wu 001; 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)

Mixed Membership Recurrent Neural Networks. - Ghazal Fazelnia; Mark Ibrahim; Ceena Modarres; Kevin Wu; John W. Paisley (CoRR, 2018)

A Modified EM Algorithm for ISAR Scatterer Trajectory Matrix Completion. - Lei Liu 014; Feng Zhou 001; Xueru Bai; John W. Paisley; Hongbing Ji (IEEE Trans. Geosci. Remote. Sens., 2018)

Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network. - Xiangyong Cao; Feng Zhou 001; Lin Xu 001; Deyu Meng; Zongben Xu; John W. Paisley (IEEE Trans. Image Process., 2018)

Global Explanations of Neural Networks: Mapping the Landscape of Predictions. - Mark Ibrahim; Melissa Louie; Ceena Modarres; John W. Paisley (AIES, 2019)

Fully Supervised Speaker Diarization. - Aonan Zhang; Quan Wang; Zhenyao Zhu; John W. Paisley; Chong Wang 002 (ICASSP, 2019)

JPEG Artifacts Reduction via Deep Convolutional Sparse Coding. - Xueyang Fu; Zheng-Jun Zha; Feng Wu 001; Xinghao Ding; John W. Paisley (ICCV, 2019)

Deep Blind Hyperspectral Image Fusion. - Wu Wang; Weihong Zeng; Yue Huang 001; Xinghao Ding; John W. Paisley (ICCV, 2019)

Random Function Priors for Correlation Modeling. - Aonan Zhang; John W. Paisley (ICML, 2019)

Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network. - Liyan Sun; Zhiwen Fan; Xinghao Ding; Yue Huang 001; John W. Paisley (IPMI, 2019)

Accurate Uncertainty Estimation and Decomposition in Ensemble Learning. - Jeremiah Z. Liu; John W. Paisley; Marianthi-Anna Kioumourtzoglou; Brent A. Coull (NeurIPS, 2019)

A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI. - Tao Tu 001; John W. Paisley; Stefan Haufe; Paul Sajda (NeurIPS, 2019)

Rain O'er Me: Synthesizing real rain to derain with data distillation. - Huangxing Lin; Yanlong Li; Xinghao Ding; Weihong Zeng; Yue Huang 001; John W. Paisley (CoRR, 2019)

Reweighted Expectation Maximization. - Adji B. Dieng; John W. Paisley (CoRR, 2019)

Learning Rate Dropout. - Huangxing Lin; Weihong Zeng; Xinghao Ding; Yue Huang 001; Chenxi Huang; John W. Paisley (CoRR, 2019)

Noise2Blur: Online Noise Extraction and Denoising. - Huangxing Lin; Weihong Zeng; Xinghao Ding; Xueyang Fu; Yue Huang 001; John W. Paisley (CoRR, 2019)

A Deep Information Sharing Network for Multi-Contrast Compressed Sensing MRI Reconstruction. - Liyan Sun; Zhiwen Fan; Xueyang Fu; Yue Huang 001; Xinghao Ding; John William Paisley (IEEE Trans. Image Process., 2019)

Online Forecasting Matrix Factorization. - San Gultekin; John W. Paisley (IEEE Trans. Signal Process., 2019)

Mixed membership recurrent neural networks for modeling customer purchases. - Ghazal Fazelnia; Mark Ibrahim; Ceena Modarres; Kevin Wu; John W. Paisley (ICAIF, 2020)

Risk Bounds for Low Cost Bipartite Ranking. - San Gultekin; John W. Paisley (UAI, 2020)

Deep Bayesian Nonparametric Factor Analysis. - Arunesh Mittal; Paul Sajda; 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)

Adaptive noise imitation for image denoising. - Huangxing Lin; Yihong Zhuang; Yue Huang 001; Xinghao Ding; Yizhou Yu; Xiaoqing Liu; John W. Paisley (CoRR, 2020)

A dual-domain deep lattice network for rapid MRI reconstruction. - Liyan Sun; Yawen Wu; Binglin Shu; Xinghao Ding; Congbo Cai; Yue Huang 001; John W. Paisley (Neurocomputing, 2020)

Rain O'er Me: Synthesizing Real Rain to Derain With Data Distillation. - Huangxing Lin; Yanlong Li; Xueyang Fu; Xinghao Ding; Yue Huang 001; John W. Paisley (IEEE Trans. Image Process., 2020)

An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection. - Liyan Sun; Jiexiang Wang; Yue Huang 001; Xinghao Ding; Hayit Greenspan; John W. Paisley (IEEE J. Biomed. Health Informatics, 2020)

A 3D Spatially Weighted Network for Segmentation of Brain Tissue From MRI. - Liyan Sun; Wenao Ma; Xinghao Ding; Yue Huang 001; Dong Liang 001; John W. Paisley (IEEE Trans. Medical Imaging, 2020)

Lightweight Pyramid Networks for Image Deraining. - Xueyang Fu; Borong Liang; Yue Huang 001; Xinghao Ding; John W. Paisley (IEEE Trans. Neural Networks Learn. Syst., 2020)

MBA: Mini-Batch AUC Optimization. - San Gultekin; Avishek Saha; Adwait Ratnaparkhi; John W. Paisley (IEEE Trans. Neural Networks Learn. Syst., 2020)

Scalable Community Detection in Massive Networks Using Aggregated Relational Data. - Timothy Jones; Yiran Jiang; John Paisley; Tian Zheng (CoRR, 2021)

Self-Verification in Image Denoising. - Huangxing Lin; Yihong Zhuang; Delu Zeng; Yue Huang 001; Xinghao Ding; John W. Paisley (CoRR, 2021)

Successive Graph Convolutional Network for Image De-raining. - Xueyang Fu; Qi Qi 005; Zheng-Jun Zha; Xinghao Ding; Feng Wu 001; John W. Paisley (Int. J. Comput. Vis., 2021)

Deep Multiscale Detail Networks for Multiband Spectral Image Sharpening. - Xueyang Fu; Wu Wang; Yue Huang 001; Xinghao Ding; John W. Paisley (IEEE Trans. Neural Networks Learn. Syst., 2021)

Probabilistic Orthogonal Matching Pursuit. - Ghazal Fazelnia; John W. Paisley (IEEE Big Data, 2022)

Bayesian Nonparametric Model Averaging Using Scalable Gaussian Process Representations. - John W. Paisley; Sebastian Rowland; Jeremiah Zhe Liu; Brent A. Coull; Marianthi-Anna Kioumourtzoglou (IEEE Big Data, 2022)

Deep Metric Representation Learning for Clinical Resting State fMRI. - Arunesh Mittal; John Paisley; Paul Sajda (EMBC, 2022)

An Interpretable Deep Classifier for Counterfactual Generation. - Wei Zhang 262; Brian Barr; John Paisley (ICAIF, 2022)

Understanding Counterfactual Generation using Maximum Mean Discrepancy. - Wei Zhang 262; Brian Barr; John Paisley (ICAIF, 2022)

Few-shot medical image segmentation using a global correlation network with discriminative embedding. - Liyan Sun; Chenxin Li; Xinghao Ding; Yue Huang 001; Zhong Chen 005; Guisheng Wang; Yizhou Yu; John W. Paisley (Comput. Biol. Medicine, 2022)

Self-Supervised Image Denoising Using Implicit Deep Denoiser Prior. - Huangxing Lin; Yihong Zhuang; Xinghao Ding; Delu Zeng; Yue Huang 001; Xiaotong Tu; John W. Paisley (AAAI, 2023)

Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks. - Arunesh Mittal; Kai Yang; Paul Sajda; John Paisley (CoRR, 2023)

Double Normalizing Flows: Flexible Bayesian Gaussian Process ODEs Learning. - Jian Xu 021; Shian Du; Junmei Yang; Xinghao Ding; John W. Paisley; Delu Zeng (CoRR, 2023)

A Multiscale Approach to Deep Blind Image Quality Assessment. - Manni Liu; Jiabin Huang 003; Delu Zeng; Xinghao Ding; John W. Paisley (IEEE Trans. Image Process., 2023)

Learning Rate Dropout. - Huangxing Lin; Weihong Zeng; Yihong Zhuang; Xinghao Ding; Yue Huang 001; John W. Paisley (IEEE Trans. Neural Networks Learn. Syst., 2023)

Gaussian Process Neural Additive Models. - Wei Zhang 262; Brian Barr; John Paisley (AAAI, 2024)

Nonlinear Kalman Filtering with Reparametrization Gradients. - San Gultekin; Brendan Kitts; Aaron Flores 001; John Paisley (ICFSP, 2024)

Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference. - Jian Xu 021; Delu Zeng; John W. Paisley (ICML, 2024)

Gaussian Process Neural Network Embeddings for Collaborative Filtering. - Wei Zhang 262; Brian Barr; John Paisley (ICMLA, 2024)

Entropy-Informed Weighting Channel Normalizing Flow. - Wei Chen; Shian Du; Shigui Li; Delu Zeng; John W. Paisley (CoRR, 2024)

Flexible Bayesian Last Layer Models Using Implicit Priors and Diffusion Posterior Sampling. - Jian Xu 021; Zhiqi Lin; Shigui Li; Min Chen; Junmei Yang; Delu Zeng; John W. Paisley (CoRR, 2024)

Information Geometry and Beta Link for Optimizing Sparse Variational Student-t Processes. - Jian Xu 021; Delu Zeng; John W. Paisley (CoRR, 2024)

Bayesian Gaussian Process ODEs via Double Normalizing Flows. - Jian Xu 021; Shian Du; Junmei Yang; Xinghao Ding; Delu Zeng; John Paisley (AISTATS, 2025)

Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling. - Jian Xu 021; Shian Du; Junmei Yang; Qianli Ma 001; Delu Zeng; John Paisley (UAI, 2025)

Fully Bayesian differential Gaussian processes through stochastic differential equations. - Jian Xu 021; Zhiqi Lin; Min Chen; Junmei Yang; Delu Zeng; John W. Paisley (Knowl. Based Syst., 2025)

Gaussian Process Tilted Nonparametric Density Estimation using Fisher Divergence Score Matching. - John Paisley; Wei Zhang 262; Brian Barr (CoRR, 2025)

Stochastic Variational Inference with Tuneable Stochastic Annealing. - John Paisley; Ghazal Fazelnia; Brian Barr (CoRR, 2025)

Dequantified Diffusion-Schrödinger Bridge for Density Ratio Estimation. - Wei Chen; Shigui Li; Jiacheng Li; Junmei Yang; John Paisley; Delu Zeng (CoRR, 2025)

Neural Bridge Processes. - Jian Xu 021; Yican Liu; Qibin Zhao; John Paisley; Delu Zeng (CoRR, 2025)