John W. Paisley

发表

Location Dependent Dirichlet Processes

Shiliang Sun, John W. Paisley, Qiuyang Liu, 2017, IScIDE.

Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations

Guillermo Sapiro, Lu Ren, Lawrence Carin, 2009, NIPS.

Stochastic Variational Inference for the HDP-HMM

John W. Paisley, Aonan Zhang, San Gultekin, 2016, AISTATS.

Removing Rain from Single Images via a Deep Detail Network

Delu Zeng, John W. Paisley, Xinghao Ding, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies

Alfred O. Hero, David B. Dunson, Bo Chen, 2010, BMC Bioinformatics.

Nonparametric factor analysis with beta process priors

Lawrence Carin, John W. Paisley, L. Carin, 2009, ICML '09.

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

David B. Dunson, Lawrence Carin, Kai Ni, 2008, IEEE Transactions on Signal Processing.

A Divide-and-Conquer Approach to Compressed Sensing MRI

John W. Paisley, Liyan Sun, Xinghao Ding, 2018, Magnetic resonance imaging.

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

John W. Paisley, Ceena Modarres, Mark Ibrahim, 2018, ArXiv.

MBA: Mini-Batch AUC Optimization

Avishek Saha, Adwait Ratnaparkhi, John W. Paisley, 2018, IEEE Transactions on Neural Networks and Learning Systems.

MEnet: A Metric Expression Network for Salient Object Segmentation

Delu Zeng, John W. Paisley, Xinghao Ding, 2018, IJCAI.

Online Variational Inference for the Hierarchical Dirichlet Process

Chong Wang, David M. Blei, John W. Paisley, 2011, AISTATS.

Stochastic variational inference pdf

Chong Wang, David M. Blei, Matthew D. Hoffman, 2012, J. Mach. Learn. Res..

Scalable Bayesian nonparametric dictionary learning

John W. Paisley, Sarper Sertoglu, J. Paisley, 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI

Qin Lin, Xiao-Ping Zhang, John W. Paisley, 2013, IEEE Transactions on Image Processing.

Compressed sensing MRI with Bayesian dictionary learning

Xiao-Ping Zhang, Feng Huang, John W. Paisley, 2013, 2013 IEEE International Conference on Image Processing.

A Nested HDP for Hierarchical Topic Models

Chong Wang, Michael I. Jordan, David M. Blei, 2013, ICLR.

Deep Bayesian Nonparametric Tracking

John W. Paisley, Aonan Zhang, J. Paisley, 2018, ICML.

Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images

David B. Dunson, Guillermo Sapiro, Lu Ren, 2012, IEEE Transactions on Image Processing.

Mixed Membership Recurrent Neural Networks pdf

Kevin Wu, Ghazal Fazelnia, John W. Paisley, 2018, ArXiv.

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

John W. Paisley, Jeremiah Zhe Liu, Brent A. Coull, 2018, ArXiv.

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

Xiao-Ping Zhang, Feng Huang, John W. Paisley, 2013, ArXiv.

Variational Bayesian Inference with Stochastic Search

Michael I. Jordan, David M. Blei, John W. Paisley, 2012, ICML.

A nonparametric Bayesian model for kernel matrix completion

Lawrence Carin, John W. Paisley, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference

Michael I. Jordan, David M. Blei, John W. Paisley, 2014, Handbook of Mixed Membership Models and Their Applications.

Nonlinear Kalman Filtering With Divergence Minimization

John W. Paisley, San Gultekin, J. Paisley, 2017, IEEE Transactions on Signal Processing.

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

Feng Wu, John W. Paisley, Xinghao Ding, 2018, ArXiv.

Variational Inference for Stick-Breaking Beta Process Priors

Lawrence Carin, David M. Blei, John W. Paisley, 2011, ICML.

The $χ$-Divergence for Approximate Inference pdf

Dustin Tran, David M. Blei, John W. Paisley, 2016, ArXiv.

PanNet: A Deep Network Architecture for Pan-Sharpening

Junfeng Yang, John W. Paisley, Yuwen Hu, 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

Markov Latent Feature Models

John W. Paisley, Aonan Zhang, J. Paisley, 2016, ICML.

A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI pdf

John W. Paisley, Liyan Sun, Xinghao Ding, 2018, ECCV.

Dirichlet process mixture models with multiple modalities

Lawrence Carin, John W. Paisley, 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

Variational Inference via \chi Upper Bound Minimization

Dustin Tran, David M. Blei, John W. Paisley, 2016, NIPS.

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

Guillermo Sapiro, Lawrence Carin, Mingyuan Zhou, 2010, 2010 IEEE International Conference on Image Processing.

Combinatorial Clustering and the Beta Negative Binomial Process

Michael I. Jordan, Tamara Broderick, John W. Paisley, 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Lightweight Pyramid Networks for Image Deraining

John W. Paisley, Xinghao Ding, Xueyang Fu, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Global Explanations of Neural Networks: Mapping the Landscape of Predictions

John W. Paisley, Ceena Modarres, Mark Ibrahim, 2019, AIES.

A Stick-Breaking Construction of the Beta Process

Lawrence Carin, John W. Paisley, Geoffrey S. Ginsburg, 2010, ICML.

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

John W. Paisley, Liyan Sun, Jiexiang Wang, 2018, ArXiv.

Nested Hierarchical Dirichlet Processes

Chong Wang, Michael I. Jordan, David M. Blei, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Stick-Breaking Beta Processes and the Poisson Process

Michael I. Jordan, David M. Blei, John W. Paisley, 2012, AISTATS.

Hierarchical Bayesian Modeling of Topics in Time-Stamped Documents

Lawrence Carin, John W. Paisley, Lu Ren, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

John W. Paisley, Liyan Sun, Xinghao Ding, 2018, IEEE Transactions on Image Processing.

CRVI: Convex Relaxation for Variational Inference

Ghazal Fazelnia, John W. Paisley, J. Paisley, 2018, ICML.

The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling

Chong Wang, David M. Blei, John W. Paisley, 2011, AISTATS.

Pan-Sharpening with a Hyper-Laplacian Penalty

Delu Zeng, John W. Paisley, Xinghao Ding, 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

Landmarking Manifolds with Gaussian Processes

John W. Paisley, Dawen Liang, J. Paisley, 2015, ICML.

A deep error correction network for compressed sensing MRI

John W. Paisley, Liyan Sun, Xinghao Ding, 2018, BMC Biomedical Engineering.

Pan-sharpening based on nonparametric Bayesian adaptive dictionary learning

Xiao-Ping Zhang, John W. Paisley, Xinghao Ding, 2013, 2013 IEEE International Conference on Image Processing.

Sparse linear regression with beta process priors

Bo Chen, Lawrence Carin, John W. Paisley, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

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

David M. Blei, Hanna M. Wallach, John W. Paisley, 2015, KDD.

Compressed Sensing MRI Using a Recursive Dilated Network

John W. Paisley, Liyan Sun, Xinghao Ding, 2018, AAAI.

A Collaborative Kalman Filter for Time-Evolving Dyadic Processes

John W. Paisley, San Gultekin, J. Paisley, 2014, 2014 IEEE International Conference on Data Mining.

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

David B. Dunson, Lawrence Carin, John W. Paisley, 2010, IEEE Transactions on Signal Processing.

Active Learning and Basis Selection for Kernel-Based Linear Models: A Bayesian Perspective

Lawrence Carin, John W. Paisley, Xuejun Liao, 2010, IEEE Transactions on Signal Processing.

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

Chong Wang, Jianfeng Gao, John W. Paisley, 2016, ICLR.

Fully Supervised Speaker Diarization

Quan Wang, Chong Wang, John W. Paisley, 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Asymptotic Simulated Annealing for Variational Inference

John W. Paisley, Aonan Zhang, San Gultekin, 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

Hidden Markov Models With Stick-Breaking Priors

Lawrence Carin, John W. Paisley, L. Carin, 2009, IEEE Transactions on Signal Processing.

Corrections to “Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds” [Dec 10 6140-6155]

David B. Dunson, Lawrence Carin, John W. Paisley, 2011, IEEE Transactions on Signal Processing.

Codebook-based Scalable Music Tagging with Poisson Matrix Factorization

Daniel P. W. Ellis, John W. Paisley, Dawen Liang, 2014, ISMIR.

Pan-sharpening with a Bayesian nonparametric dictionary learning model

John W. Paisley, Xinghao Ding, Yue Huang, 2014, AISTATS.

A fusion-based enhancing method for weakly illuminated images

Delu Zeng, John W. Paisley, Xinghao Ding, 2016, Signal Process..

Markov Mixed Membership Models

John W. Paisley, Aonan Zhang, J. Paisley, 2015, ICML.

Successive Graph Convolutional Network for Image De-raining

Zheng-Jun Zha, Feng Wu, John W. Paisley, 2021, International Journal of Computer Vision.