b'John Paisley'

research

∙ 09/17/2023

Double Normalizing Flows: Flexible Bayesian Gaussian Process ODEs Learning

Recently, Gaussian processes have been utilized to model the vector fiel...

0 Jian Xu, et al. ∙

research

∙ 03/14/2023

Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks

Several approximate inference methods have been proposed for deep discre...

0 Arunesh Mittal, et al. ∙

research

∙ 03/08/2023

Nonlinear Kalman Filtering with Reparametrization Gradients

We introduce a novel nonlinear Kalman filter that utilizes reparametriza...

0 San Gultekin, et al. ∙

research

∙ 11/01/2021

Self-Verification in Image Denoising

We devise a new regularization, called self-verification, for image deno...

0 Huangxing Lin, et al. ∙

research

∙ 09/24/2021

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

Environmental health researchers may aim to identify exposure patterns t...

0 Elizabeth A. Gibson, et al. ∙

research

∙ 11/30/2020

Adaptive noise imitation for image denoising

The effectiveness of existing denoising algorithms typically relies on a...

4 Huangxing Lin, et al. ∙

research

∙ 11/14/2020

Bayesian recurrent state space model for rs-fMRI

We propose a hierarchical Bayesian recurrent state space model for model...

0 Arunesh Mittal, et al. ∙

research

∙ 11/09/2020

Deep Bayesian Nonparametric Factor Analysis

We propose a deep generative factor analysis model with beta process pri...

0 Arunesh Mittal, et al. ∙

research

∙ 12/03/2019

Noise2Blur: Online Noise Extraction and Denoising

We propose a new framework called Noise2Blur (N2B) for training robust i...

25 Huangxing Lin, et al. ∙

research

∙ 12/02/2019

Risk Bounds for Low Cost Bipartite Ranking

Bipartite ranking is an important supervised learning problem; however, ...

0 San Gultekin, et al. ∙

research

∙ 11/30/2019

Learning Rate Dropout

The performance of a deep neural network is highly dependent on its trai...

0 Huangxing Lin, et al. ∙

research

∙ 11/11/2019

Accurate Uncertainty Estimation and Decomposition in Ensemble Learning

Ensemble learning is a standard approach to building machine learning sy...

0 Jeremiah Zhe Liu, et al. ∙

research

∙ 06/13/2019

Reweighted Expectation Maximization

Training deep generative models with maximum likelihood remains a challe...

0 Adji B. Dieng, et al. ∙

research

∙ 05/09/2019

Random Function Priors for Correlation Modeling

The likelihood model of many high dimensional data X_n can be expressed ...

0 Aonan Zhang, et al. ∙

research

∙ 04/09/2019

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

We present a supervised technique for learning to remove rain from image...

0 Huangxing Lin, et al. ∙

research

∙ 04/01/2019

Adaptive Ensemble Learning of Spatiotemporal Processes with Calibrated Predictive Uncertainty: A Bayesian Nonparametric Approach

Ensemble learning is a mainstay in modern data science practice. Convent...

0 Jeremiah Zhe Liu, et al. ∙

research

∙ 02/06/2019

Global Explanations of Neural Networks: Mapping the Landscape of Predictions

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

0 Mark Ibrahim, et al. ∙

research

∙ 12/23/2018

Mixed Membership Recurrent Neural Networks

Models for sequential data such as the recurrent neural network (RNN) of...

0 Ghazal Fazelnia, et al. ∙

research

∙ 12/08/2018

Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process

Ensemble learning is a mainstay in modern data science practice. Convent...

0 Jeremiah Zhe Liu, et al. ∙

research

∙ 11/21/2018

A Deep Tree-Structured Fusion Model for Single Image Deraining

We propose a simple yet effective deep tree-structured fusion model base...

14 Xueyang Fu, et al. ∙

research

∙ 11/15/2018

Towards Explainable Deep Learning for Credit Lending: A Case Study

Deep learning adoption in the financial services industry has been limit...

0 Ceena Modarres, et al. ∙

research

∙ 10/25/2018

An Adversarial Learning Approach to Medical Image Synthesis for Lesion Removal

The analysis of lesion within medical image data is desirable for effici...

0 Liyan Sun, et al. ∙

research

∙ 10/10/2018

Fully Supervised Speaker Diarization

In this paper, we propose a fully supervised speaker diarization approac...

0 Aonan Zhang, et al. ∙

research

∙ 05/29/2018

MBA: Mini-Batch AUC Optimization

Area under the receiver operating characteristics curve (AUC) is an impo...

0 San Gultekin, et al. ∙

research

∙ 05/16/2018

Lightweight Pyramid Networks for Image Deraining

Existing deep convolutional neural networks have found major success in ...

0 Xueyang Fu, et al. ∙

research

∙ 05/15/2018

MEnet: A Metric Expression Network for Salient Object Segmentation

Recent CNN-based saliency models have achieved great performance on publ...

0 Shulian Cai, et al. ∙

research

∙ 05/06/2018

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

The need for fast acquisition and automatic analysis of MRI data is grow...

0 Liyan Sun, et al. ∙

research

∙ 04/10/2018

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

In multi-contrast magnetic resonance imaging (MRI), compressed sensing t...

0 Liyan Sun, et al. ∙

research

∙ 04/04/2018

A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI

Compressed sensing MRI is a classic inverse problem in the field of comp...

0 Zhiwen Fan, et al. ∙

research

∙ 03/27/2018

A Divide-and-Conquer Approach to Compressed Sensing MRI

Compressed sensing (CS) theory assures us that we can accurately reconst...

0 Liyan Sun, et al. ∙

research

∙ 03/23/2018

A Deep Error Correction Network for Compressed Sensing MRI

Compressed sensing for magnetic resonance imaging (CS-MRI) exploits imag...

0 Liyan Sun, et al. ∙

research

∙ 12/23/2017

Online Forecasting Matrix Factorization

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

0 San Gultekin, et al. ∙

research

∙ 07/02/2017

Location Dependent Dirichlet Processes

Dirichlet processes (DP) are widely applied in Bayesian nonparametric mo...

0 Shiliang Sun, et al. ∙

research

∙ 05/01/2017

Nonlinear Kalman Filtering with Divergence Minimization

We consider the nonlinear Kalman filtering problem using Kullback-Leible...

0 San Gultekin, et al. ∙

research

∙ 11/05/2016

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

In this paper, we propose TopicRNN, a recurrent neural network (RNN)-bas...

0 Adji B. Dieng, et al. ∙

research

∙ 11/01/2016

Variational Inference via χ-Upper Bound Minimization

Variational inference (VI) is widely used as an efficient alternative to...

0 Adji B. Dieng, et al. ∙

research

∙ 09/07/2016

Clearing the Skies: A deep network architecture for single-image rain removal

We introduce a deep network architecture called DerainNet for removing r...

0 Xueyang Fu, et al. ∙

research

∙ 06/10/2015

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

We present a Bayesian tensor factorization model for inferring latent gr...

0 Aaron Schein, et al. ∙

research

∙ 05/25/2015

Stochastic Annealing for Variational Inference

We empirically evaluate a stochastic annealing strategy for Bayesian pos...

0 San Gultekin, et al. ∙

research

∙ 01/22/2015

A Collaborative Kalman Filter for Time-Evolving Dyadic Processes

We present the collaborative Kalman filter (CKF), a dynamic model for co...

0 San Gultekin, et al. ∙

research

∙ 01/16/2013

A Nested HDP for Hierarchical Topic Models

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

0 John Paisley, et al. ∙

research

∙ 10/25/2012

Nested Hierarchical Dirichlet Processes

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

0 John Paisley, et al. ∙

research

∙ 06/29/2012

Stochastic Variational Inference

We develop stochastic variational inference, a scalable algorithm for ap...

0 Matt Hoffman, et al. ∙

research

∙ 06/27/2012

Variational Bayesian Inference with Stochastic Search

Mean-field variational inference is a method for approximate Bayesian po...

0 John Paisley, et al. ∙

research

∙ 11/08/2011

Combinatorial clustering and the beta negative binomial process

We develop a Bayesian nonparametric approach to a general family of late...

0 Tamara Broderick, et al. ∙

research

∙ 09/02/2011

The Stick-Breaking Construction of the Beta Process as a Poisson Process

We show that the stick-breaking construction of the beta process due to ...

0 John Paisley, et al. ∙

research

∙ 03/24/2011

The Discrete Infinite Logistic Normal Distribution

We present the discrete infinite logistic normal distribution (DILN), a ...

0 John Paisley, et al. ∙

John Paisley

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