John W. Paisley

According to our database1, John W. Paisley authored at least 86 papers between 2007 and 2020.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
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PhD thesis 
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Homepages:

On csauthors.net:

Bibliography

2020
Lightweight Pyramid Networks for Image Deraining.
IEEE Trans. Neural Networks Learn. Syst., 2020

A 3D Spatially Weighted Network for Segmentation of Brain Tissue From MRI.
IEEE Trans. Medical Imaging, 2020

A dual-domain deep lattice network for rapid MRI reconstruction.
Neurocomputing, 2020

2019
Online Forecasting Matrix Factorization.
IEEE Trans. Signal Process., 2019

A Deep Information Sharing Network for Multi-Contrast Compressed Sensing MRI Reconstruction.
IEEE Trans. Image Process., 2019

Noise2Blur: Online Noise Extraction and Denoising.
CoRR, 2019

Risk Bounds for Low Cost Bipartite Ranking.
CoRR, 2019

Learning Rate Dropout.
CoRR, 2019

Reweighted Expectation Maximization.
CoRR, 2019

Rain O'er Me: Synthesizing real rain to derain with data distillation.
CoRR, 2019

Accurate Uncertainty Estimation and Decomposition in Ensemble Learning.
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.
Proceedings of the Information Processing in Medical Imaging, 2019

Random Function Priors for Correlation Modeling.
Proceedings of the 36th International Conference on Machine Learning, 2019

Deep Blind Hyperspectral Image Fusion.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

JPEG Artifacts Reduction via Deep Convolutional Sparse Coding.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Fully Supervised Speaker Diarization.
Proceedings of the IEEE International Conference on Acoustics, 2019

Global Explanations of Neural Networks: Mapping the Landscape of Predictions.
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.
IEEE Trans. Image Process., 2018

A Modified EM Algorithm for ISAR Scatterer Trajectory Matrix Completion.
IEEE Trans. Geosci. Remote. Sens., 2018

Mixed Membership Recurrent Neural Networks.
CoRR, 2018

Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process.
CoRR, 2018

A Deep Tree-Structured Fusion Model for Single Image Deraining.
CoRR, 2018

Towards Explainable Deep Learning for Credit Lending: A Case Study.
CoRR, 2018

An Adversarial Learning Approach to Medical Image Synthesis for Lesion Removal.
CoRR, 2018

MBA: Mini-Batch AUC Optimization.
CoRR, 2018

A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction.
CoRR, 2018

A Divide-and-Conquer Approach to Compressed Sensing MRI.
CoRR, 2018

A Deep Error Correction Network for Compressed Sensing MRI.
CoRR, 2018

MEnet: A Metric Expression Network for Salient Object Segmentation.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Deep Bayesian Nonparametric Tracking.
Proceedings of the 35th International Conference on Machine Learning, 2018

CRVI: Convex Relaxation for Variational Inference.
Proceedings of the 35th International Conference on Machine Learning, 2018

Asymptotic Simulated Annealing for Variational Inference.
Proceedings of the IEEE Global Communications Conference, 2018

A Segmentation-Aware Deep Fusion Network for Compressed Sensing MRI.
Proceedings of the Computer Vision - ECCV 2018, 2018

Compressed Sensing MRI Using a Recursive Dilated Network.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Nonlinear Kalman Filtering With Divergence Minimization.
IEEE Trans. Signal Process., 2017

Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal.
IEEE Trans. Image Process., 2017

Hyperspectral Image Segmentation with Markov Random Fields and a Convolutional Neural Network.
CoRR, 2017

Variational Inference via \chi Upper Bound Minimization.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Location Dependent Dirichlet Processes.
Proceedings of the Intelligence Science and Big Data Engineering, 2017

TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency.
Proceedings of the 5th International Conference on Learning Representations, 2017

PanNet: A Deep Network Architecture for Pan-Sharpening.
Proceedings of the IEEE International Conference on Computer Vision, 2017

Removing Rain from Single Images via a Deep Detail Network.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016
A fusion-based enhancing method for weakly illuminated images.
Signal Process., 2016

The $χ$-Divergence for Approximate Inference.
CoRR, 2016

Markov Latent Feature Models.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Stochastic Variational Inference for the HDP-HMM.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Nested Hierarchical Dirichlet Processes.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Combinatorial Clustering and the Beta Negative Binomial Process.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts.
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Markov Mixed Membership Models.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Landmarking Manifolds with Gaussian Processes.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Pan-Sharpening with a Hyper-Laplacian Penalty.
Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

Scalable Bayesian nonparametric dictionary learning.
Proceedings of the 23rd European Signal Processing Conference, 2015

2014
Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference.
Proceedings of the Handbook of Mixed Membership Models and Their Applications., 2014

Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI.
IEEE Trans. Image Process., 2014

Codebook-based Scalable Music Tagging with Poisson Matrix Factorization.
Proceedings of the 15th International Society for Music Information Retrieval Conference, 2014

A Collaborative Kalman Filter for Time-Evolving Dyadic Processes.
Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

Pan-sharpening with a Bayesian nonparametric dictionary learning model.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
Stochastic variational inference.
J. Mach. Learn. Res., 2013

MR Image Reconstruction from Undersampled k-Space with Bayesian Dictionary Learning
CoRR, 2013

A Nested HDP for Hierarchical Topic Models
Proceedings of the 1st International Conference on Learning Representations, 2013

Pan-sharpening based on nonparametric Bayesian adaptive dictionary learning.
Proceedings of the IEEE International Conference on Image Processing, 2013

Compressed sensing MRI with Bayesian dictionary learning.
Proceedings of the IEEE International Conference on Image Processing, 2013

2012
Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images.
IEEE Trans. Image Process., 2012

Stick-Breaking Beta Processes and the Poisson Process.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Variational Bayesian Inference with Stochastic Search.
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".
IEEE Trans. Signal Process., 2011

Online Variational Inference for the Hierarchical Dirichlet Process.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Variational Inference for Stick-Breaking Beta Process Priors.
Proceedings of the 28th International Conference on Machine Learning, 2011

2010
Machine Learning with Dirichlet and Beta Process Priors: Theory and Applications.
PhD thesis, 2010

Active learning and basis selection for kernel-based linear models: a Bayesian perspective.
IEEE Trans. Signal Process., 2010

Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds.
IEEE Trans. Signal Process., 2010

Hierarchical Bayesian Modeling of Topics in Time-Stamped Documents.
IEEE Trans. Pattern Anal. Mach. Intell., 2010

Bayesian Inference of the Number of Factors in Gene-Expression Analysis: Application to Human Virus Challenge Studies.
BMC Bioinform., 2010

A Stick-Breaking Construction of the Beta Process.
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.
Proceedings of the International Conference on Image Processing, 2010

A nonparametric Bayesian model for kernel matrix completion.
Proceedings of the IEEE International Conference on Acoustics, 2010

Sparse linear regression with beta process priors.
Proceedings of the IEEE International Conference on Acoustics, 2010

2009
Hidden Markov models with stick-breaking priors.
IEEE Trans. Signal Process., 2009

Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations.
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.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Dirichlet process mixture models with multiple modalities.
Proceedings of the IEEE International Conference on Acoustics, 2009

2008
Multi-Task Learning for Analyzing and Sorting Large Databases of Sequential Data.
IEEE Trans. Signal Process., 2008

2007
Music Analysis Using Hidden Markov Mixture Models.
IEEE Trans. Signal Process., 2007

Dirichlet Process HMM Mixture Models with Application to Music Analysis.
Proceedings of the IEEE International Conference on Acoustics, 2007


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