
Noise2Blur: Online Noise Extraction and Denoising
We propose a new framework called Noise2Blur (N2B) for training robust i...
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A Deep TreeStructured Fusion Model for Single Image Deraining
We propose a simple yet effective deep treestructured fusion model base...
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Location Dependent Dirichlet Processes
Dirichlet processes (DP) are widely applied in Bayesian nonparametric mo...
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Nonlinear Kalman Filtering with Divergence Minimization
We consider the nonlinear Kalman filtering problem using KullbackLeible...
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Variational Inference via χUpper Bound Minimization
Variational inference (VI) is widely used as an efficient alternative to...
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Clearing the Skies: A deep network architecture for singleimage rain removal
We introduce a deep network architecture called DerainNet for removing r...
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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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Stochastic Annealing for Variational Inference
We empirically evaluate a stochastic annealing strategy for Bayesian pos...
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A Collaborative Kalman Filter for TimeEvolving Dyadic Processes
We present the collaborative Kalman filter (CKF), a dynamic model for co...
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A Nested HDP for Hierarchical Topic Models
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchic...
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Nested Hierarchical Dirichlet Processes
We develop a nested hierarchical Dirichlet process (nHDP) for hierarchic...
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Stochastic Variational Inference
We develop stochastic variational inference, a scalable algorithm for ap...
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Variational Bayesian Inference with Stochastic Search
Meanfield variational inference is a method for approximate Bayesian po...
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Combinatorial clustering and the beta negative binomial process
We develop a Bayesian nonparametric approach to a general family of late...
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The StickBreaking Construction of the Beta Process as a Poisson Process
We show that the stickbreaking construction of the beta process due to ...
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The Discrete Infinite Logistic Normal Distribution
We present the discrete infinite logistic normal distribution (DILN), a ...
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TopicRNN: A Recurrent Neural Network with LongRange Semantic Dependency
In this paper, we propose TopicRNN, a recurrent neural network (RNN)bas...
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Online Forecasting Matrix Factorization
In this paper the problem of forecasting high dimensional time series is...
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A Deep Error Correction Network for Compressed Sensing MRI
Compressed sensing for magnetic resonance imaging (CSMRI) exploits imag...
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A DivideandConquer Approach to Compressed Sensing MRI
Compressed sensing (CS) theory assures us that we can accurately reconst...
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A Segmentationaware Deep Fusion Network for Compressed Sensing MRI
Compressed sensing MRI is a classic inverse problem in the field of comp...
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A Deep Information Sharing Network for Multicontrast Compressed Sensing MRI Reconstruction
In multicontrast magnetic resonance imaging (MRI), compressed sensing t...
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Joint CSMRI 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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Lightweight Pyramid Networks for Image Deraining
Existing deep convolutional neural networks have found major success in ...
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MEnet: A Metric Expression Network for Salient Object Segmentation
Recent CNNbased saliency models have achieved great performance on publ...
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MBA: MiniBatch AUC Optimization
Area under the receiver operating characteristics curve (AUC) is an impo...
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Fully Supervised Speaker Diarization
In this paper, we propose a fully supervised speaker diarization approac...
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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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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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Adaptive and Calibrated Ensemble Learning with Dependent Tailfree Process
Ensemble learning is a mainstay in modern data science practice. Convent...
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Mixed Membership Recurrent Neural Networks
Models for sequential data such as the recurrent neural network (RNN) of...
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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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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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Random Function Priors for Correlation Modeling
The likelihood model of many high dimensional data X_n can be expressed ...
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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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Reweighted Expectation Maximization
Training deep generative models with maximum likelihood remains a challe...
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Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Ensemble learning is a standard approach to building machine learning sy...
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Learning Rate Dropout
The performance of a deep neural network is highly dependent on its trai...
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Risk Bounds for Low Cost Bipartite Ranking
Bipartite ranking is an important supervised learning problem; however, ...
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John Paisley
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Assistant professor in the Department of Electrical Engineering at Columbia University. Member of the Data Science Institute at Columbia.