Search Results for author: John Paisley

Found 49 papers, 12 papers with code

Self-Verification in Image Denoising

no code implementations1 Nov 2021 Huangxing Lin, Yihong Zhuang, Delu Zeng, Yue Huang, Xinghao Ding, John Paisley

Specifically, we treat the output of the network as a ``prior'' that we denoise again after ``re-noising''.

Image Denoising

Bayesian non-parametric non-negative matrix factorization for pattern identification in environmental mixtures

no code implementations24 Sep 2021 Elizabeth A. Gibson, Sebastian T. Rowland, Jeff Goldsmith, John Paisley, Julie B. Herbstman, Marianthi-Anna Kiourmourtzoglou

Environmental health researchers may aim to identify exposure patterns that represent sources, product use, or behaviors that give rise to mixtures of potentially harmful environmental chemical exposures.

Adaptive noise imitation for image denoising

no code implementations30 Nov 2020 Huangxing Lin, Yihong Zhuang, Yue Huang, Xinghao Ding, Yizhou Yu, Xiaoqing Liu, John Paisley

Coupling the noisy data output from ADANI with the corresponding ground-truth, a denoising CNN is then trained in a fully-supervised manner.

Image Denoising

Bayesian recurrent state space model for rs-fMRI

no code implementations14 Nov 2020 Arunesh Mittal, Scott Linderman, John Paisley, Paul Sajda

We evaluate our method on the ADNI2 dataset by inferring latent state patterns corresponding to altered neural circuits in individuals with Mild Cognitive Impairment (MCI).

Deep Bayesian Nonparametric Factor Analysis

no code implementations9 Nov 2020 Arunesh Mittal, Paul Sajda, John Paisley

We propose a deep generative factor analysis model with beta process prior that can approximate complex non-factorial distributions over the latent codes.

Noise2Blur: Online Noise Extraction and Denoising

no code implementations3 Dec 2019 Huangxing Lin, Weihong Zeng, Xinghao Ding, Xueyang Fu, Yue Huang, John Paisley

Using the new image pair, the denoising network learns to generate clean and high-quality images from noisy observations.

Image Denoising

Risk Bounds for Low Cost Bipartite Ranking

no code implementations2 Dec 2019 San Gultekin, John Paisley

Bipartite ranking is an important supervised learning problem; however, unlike regression or classification, it has a quadratic dependence on the number of samples.

Generalization Bounds

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

1 code implementation NeurIPS 2019 Tao Tu, John Paisley, Stefan Haufe, Paul Sajda

In this study, we develop a linear state-space model to infer the effective connectivity in a distributed brain network based on simultaneously recorded EEG and fMRI data.


Learning Rate Dropout

2 code implementations30 Nov 2019 Huangxing Lin, Weihong Zeng, Xinghao Ding, Yue Huang, Chenxi Huang, John Paisley

The uncertainty of the descent path helps the model avoid saddle points and bad local minima.

Accurate Uncertainty Estimation and Decomposition in Ensemble Learning

no code implementations NeurIPS 2019 Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou, Brent Coull

We introduce a Bayesian nonparametric ensemble (BNE) approach that augments an existing ensemble model to account for different sources of model uncertainty.

Bias Detection Ensemble Learning

Reweighted Expectation Maximization

1 code implementation13 Jun 2019 Adji B. Dieng, John Paisley

The typical workaround is to use variational inference (VI) and maximize a lower bound to the log marginal likelihood of the data.

Bayesian Inference Density Estimation +1

Random Function Priors for Correlation Modeling

1 code implementation9 May 2019 Aonan Zhang, John Paisley

The likelihood model of high dimensional data $X_n$ can often be expressed as $p(X_n|Z_n,\theta)$, where $\theta\mathrel{\mathop:}=(\theta_k)_{k\in[K]}$ is a collection of hidden features shared across objects, indexed by $n$, and $Z_n$ is a non-negative factor loading vector with $K$ entries where $Z_{nk}$ indicates the strength of $\theta_k$ used to express $X_n$.

Variational Inference

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

no code implementations9 Apr 2019 Huangxing Lin, Yanlong Li, Xinghao Ding, Weihong Zeng, Yue Huang, John Paisley

We present a supervised technique for learning to remove rain from images without using synthetic rain software.

Rain Removal

Global Explanations of Neural Networks: Mapping the Landscape of Predictions

1 code implementation6 Feb 2019 Mark Ibrahim, Melissa Louie, Ceena Modarres, John Paisley

A barrier to the wider adoption of neural networks is their lack of interpretability.

Mixed Membership Recurrent Neural Networks

no code implementations23 Dec 2018 Ghazal Fazelnia, Mark Ibrahim, Ceena Modarres, Kevin Wu, John Paisley

Models for sequential data such as the recurrent neural network (RNN) often implicitly model a sequence as having a fixed time interval between observations and do not account for group-level effects when multiple sequences are observed.

A Deep Tree-Structured Fusion Model for Single Image Deraining

no code implementations21 Nov 2018 Xueyang Fu, Qi Qi, Yue Huang, Xinghao Ding, Feng Wu, John Paisley

We propose a simple yet effective deep tree-structured fusion model based on feature aggregation for the deraining problem.

Single Image Deraining

Towards Explainable Deep Learning for Credit Lending: A Case Study

no code implementations15 Nov 2018 Ceena Modarres, Mark Ibrahim, Melissa Louie, John Paisley

Deep learning adoption in the financial services industry has been limited due to a lack of model interpretability.

An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection

no code implementations25 Oct 2018 Liyan Sun, Jiexiang Wang, Yue Huang, Xinghao Ding, Hayit Greenspan, John Paisley

Being able to provide a "normal" counterpart to a medical image can provide useful side information for medical imaging tasks like lesion segmentation or classification validated by our experiments.

Data Augmentation General Classification +4

Fully Supervised Speaker Diarization

1 code implementation10 Oct 2018 Aonan Zhang, Quan Wang, Zhenyao Zhu, John Paisley, Chong Wang

In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN).

Speaker Diarization

Deep Bayesian Nonparametric Tracking

no code implementations ICML 2018 Aonan Zhang, John Paisley

Time-series data often exhibit irregular behavior, making them hard to analyze and explain with a simple dynamic model.

Time Series

CRVI: Convex Relaxation for Variational Inference

no code implementations ICML 2018 Ghazal Fazelnia, John Paisley

In this paper, we introduce a new approach to solving the variational inference optimization based on convex relaxation and semidefinite programming.

Inference Optimization Variational Inference

MBA: Mini-Batch AUC Optimization

no code implementations29 May 2018 San Gultekin, Avishek Saha, Adwait Ratnaparkhi, John Paisley

Area under the receiver operating characteristics curve (AUC) is an important metric for a wide range of signal processing and machine learning problems, and scalable methods for optimizing AUC have recently been proposed.

Lightweight Pyramid Networks for Image Deraining

no code implementations16 May 2018 Xueyang Fu, Borong Liang, Yue Huang, Xinghao Ding, John Paisley

In this paper, we propose a lightweight pyramid of networks (LPNet) for single image deraining.

Single Image Deraining

Ro-SOS: Metric Expression Network (MEnet) for Robust Salient Object Segmentation

1 code implementation15 May 2018 Delu Zeng, Yixuan He, Li Liu, Zhihong Chen, Jiabin Huang, Jie Chen, John Paisley

In this paper, we propose an end-to-end generic salient object segmentation model called Metric Expression Network (MEnet) to deal with saliency detection with the tolerance of distortion.

Saliency Detection Semantic Segmentation

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

no code implementations10 Apr 2018 Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John Paisley

In multi-contrast magnetic resonance imaging (MRI), compressed sensing theory can accelerate imaging by sampling fewer measurements within each contrast.

MRI Reconstruction

A Divide-and-Conquer Approach to Compressed Sensing MRI

no code implementations27 Mar 2018 Liyan Sun, Zhiwen Fan, Xinghao Ding, Congbo Cai, Yue Huang, John Paisley

Compressed sensing (CS) theory assures us that we can accurately reconstruct magnetic resonance images using fewer k-space measurements than the Nyquist sampling rate requires.

A Deep Error Correction Network for Compressed Sensing MRI

no code implementations23 Mar 2018 Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John Paisley

Existing CS-MRI algorithms can serve as the template module for guiding the reconstruction.

MRI Reconstruction

Online Forecasting Matrix Factorization

no code implementations23 Dec 2017 San Gultekin, John Paisley

In this paper the problem of forecasting high dimensional time series is considered.

Time Series

Variational Inference via \chi Upper Bound Minimization

no code implementations NeurIPS 2017 Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David Blei

In this paper we propose CHIVI, a black-box variational inference algorithm that minimizes $D_{\chi}(p || q)$, the $\chi$-divergence from $p$ to $q$.

Variational Inference

PanNet: A Deep Network Architecture for Pan-Sharpening

no code implementations ICCV 2017 Junfeng Yang, Xueyang Fu, Yuwen Hu, Yue Huang, Xinghao Ding, John Paisley

We incorporate domain-specific knowledge to design our PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation.

Removing Rain From Single Images via a Deep Detail Network

no code implementations CVPR 2017 Xueyang Fu, Jia-Bin Huang, Delu Zeng, Yue Huang, Xinghao Ding, John Paisley

We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN).

Denoising Rain Removal

Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network

1 code implementation1 May 2017 Xiangyong Cao, Feng Zhou, Lin Xu, Deyu Meng, Zongben Xu, John Paisley

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework.

Classification General Classification +1

Nonlinear Kalman Filtering with Divergence Minimization

no code implementations1 May 2017 San Gultekin, John Paisley

We consider the nonlinear Kalman filtering problem using Kullback-Leibler (KL) and $\alpha$-divergence measures as optimization criteria.

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

1 code implementation5 Nov 2016 Adji B. Dieng, Chong Wang, Jianfeng Gao, John Paisley

The proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local (syntactic) dependencies using an RNN and global (semantic) dependencies using latent topics.

Language Modelling Sentiment Analysis +1

Variational Inference via $χ$-Upper Bound Minimization

no code implementations1 Nov 2016 Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David M. Blei

In this paper we propose CHIVI, a black-box variational inference algorithm that minimizes $D_{\chi}(p || q)$, the $\chi$-divergence from $p$ to $q$.

Variational Inference

Pan-Sharpening With a Hyper-Laplacian Penalty

no code implementations ICCV 2015 Yiyong Jiang, Xinghao Ding, Delu Zeng, Yue Huang, John Paisley

Our objective incorporates the L1/2-norm in a way that can leverage recent computationally efficient methods, and L1 for which the alternating direction method of multipliers can be used.

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

1 code implementation10 Jun 2015 Aaron Schein, John Paisley, David M. Blei, Hanna Wallach

We demonstrate that our model's predictive performance is better than that of standard non-negative tensor factorization methods.

Stochastic Annealing for Variational Inference

no code implementations25 May 2015 San Gultekin, Aonan Zhang, John Paisley

We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference.

Variational Inference

A Collaborative Kalman Filter for Time-Evolving Dyadic Processes

no code implementations22 Jan 2015 San Gultekin, John Paisley

Using the matrix factorization approach to collaborative filtering, the CKF accounts for time evolution by modeling each low-dimensional latent embedding as a multidimensional Brownian motion.

Collaborative Filtering

Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI

no code implementations12 Feb 2013 Yue Huang, John Paisley, Qin Lin, Xinghao Ding, Xueyang Fu, Xiao-Ping Zhang

The size of the dictionary and the patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables.

Denoising Dictionary Learning +2

Nested Hierarchical Dirichlet Processes

no code implementations25 Oct 2012 John Paisley, Chong Wang, David M. Blei, Michael. I. Jordan

We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling.

Variational Inference

Stochastic Variational Inference

2 code implementations29 Jun 2012 Matt Hoffman, David M. Blei, Chong Wang, John Paisley

We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions.

Topic Models Variational Inference

Variational Bayesian Inference with Stochastic Search

no code implementations27 Jun 2012 John Paisley, David Blei, Michael Jordan

This requires the ability to integrate a sum of terms in the log joint likelihood using this factorized distribution.

Bayesian Inference Stochastic Optimization +1

Combinatorial clustering and the beta negative binomial process

no code implementations8 Nov 2011 Tamara Broderick, Lester Mackey, John Paisley, Michael. I. Jordan

We show that the NBP is conjugate to the beta process, and we characterize the posterior distribution under the beta-negative binomial process (BNBP) and hierarchical models based on the BNBP (the HBNBP).

Object Recognition Semantic Segmentation

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