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09/17/2023
Double Normalizing Flows: Flexible Bayesian Gaussian Process ODEs Learning
Recently, Gaussian processes have been utilized to model the vector fiel...
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03/14/2023
Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks
Several approximate inference methods have been proposed for deep discre...
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03/08/2023
Nonlinear Kalman Filtering with Reparametrization Gradients
We introduce a novel nonlinear Kalman filter that utilizes reparametriza...
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11/01/2021
Self-Verification in Image Denoising
We devise a new regularization, called self-verification, for image deno...
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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...
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11/30/2020
Adaptive noise imitation for image denoising
The effectiveness of existing denoising algorithms typically relies on a...
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11/14/2020
Bayesian recurrent state space model for rs-fMRI
We propose a hierarchical Bayesian recurrent state space model for model...
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11/09/2020
Deep Bayesian Nonparametric Factor Analysis
We propose a deep generative factor analysis model with beta process pri...
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12/03/2019
Noise2Blur: Online Noise Extraction and Denoising
We propose a new framework called Noise2Blur (N2B) for training robust i...
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12/02/2019
Risk Bounds for Low Cost Bipartite Ranking
Bipartite ranking is an important supervised learning problem; however, ...
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11/30/2019
Learning Rate Dropout
The performance of a deep neural network is highly dependent on its trai...
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11/11/2019
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Ensemble learning is a standard approach to building machine learning sy...
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06/13/2019
Reweighted Expectation Maximization
Training deep generative models with maximum likelihood remains a challe...
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05/09/2019
Random Function Priors for Correlation Modeling
The likelihood model of many high dimensional data X_n can be expressed ...
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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...
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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...
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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...
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12/23/2018
Mixed Membership Recurrent Neural Networks
Models for sequential data such as the recurrent neural network (RNN) of...
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12/08/2018
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process
Ensemble learning is a mainstay in modern data science practice. Convent...
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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...
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11/15/2018
Towards Explainable Deep Learning for Credit Lending: A Case Study
Deep learning adoption in the financial services industry has been limit...
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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...
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10/10/2018
Fully Supervised Speaker Diarization
In this paper, we propose a fully supervised speaker diarization approac...
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05/29/2018
MBA: Mini-Batch AUC Optimization
Area under the receiver operating characteristics curve (AUC) is an impo...
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05/16/2018
Lightweight Pyramid Networks for Image Deraining
Existing deep convolutional neural networks have found major success in ...
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05/15/2018
MEnet: A Metric Expression Network for Salient Object Segmentation
Recent CNN-based saliency models have achieved great performance on publ...
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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...
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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...
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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...
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03/27/2018
A Divide-and-Conquer Approach to Compressed Sensing MRI
Compressed sensing (CS) theory assures us that we can accurately reconst...
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03/23/2018
A Deep Error Correction Network for Compressed Sensing MRI
Compressed sensing for magnetic resonance imaging (CS-MRI) exploits imag...
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12/23/2017
Online Forecasting Matrix Factorization
In this paper the problem of forecasting high dimensional time series is...
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07/02/2017
Location Dependent Dirichlet Processes
Dirichlet processes (DP) are widely applied in Bayesian nonparametric mo...
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05/01/2017
Nonlinear Kalman Filtering with Divergence Minimization
We consider the nonlinear Kalman filtering problem using Kullback-Leible...
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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...
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11/01/2016
Variational Inference via χ-Upper Bound Minimization
Variational inference (VI) is widely used as an efficient alternative to...
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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...
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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...
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05/25/2015
Stochastic Annealing for Variational Inference
We empirically evaluate a stochastic annealing strategy for Bayesian pos...
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01/22/2015
A Collaborative Kalman Filter for Time-Evolving Dyadic Processes
We present the collaborative Kalman filter (CKF), a dynamic model for co...
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01/16/2013
A Nested HDP for Hierarchical Topic Models
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchic...
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10/25/2012
Nested Hierarchical Dirichlet Processes
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchic...
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06/29/2012
Stochastic Variational Inference
We develop stochastic variational inference, a scalable algorithm for ap...
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06/27/2012
Variational Bayesian Inference with Stochastic Search
Mean-field variational inference is a method for approximate Bayesian po...
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11/08/2011
Combinatorial clustering and the beta negative binomial process
We develop a Bayesian nonparametric approach to a general family of late...
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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 ...
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03/24/2011
