John Paisley

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Columbia University ∙ berkeley college

Assistant professor in the Department of Electrical Engineering at Columbia University. Member of the Data Science Institute at Columbia.

Noise2Blur: Online Noise Extraction and Denoising

We propose a new framework called Noise2Blur (N2B) for training robust i...
12/03/2019 ∙ by Huangxing Lin, et al. ∙ 25 ∙ share

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A Deep Tree-Structured Fusion Model for Single Image Deraining

We propose a simple yet effective deep tree-structured fusion model base...
11/21/2018 ∙ by Xueyang Fu, et al. ∙ 14 ∙ share

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Location Dependent Dirichlet Processes

Dirichlet processes (DP) are widely applied in Bayesian nonparametric mo...
07/02/2017 ∙ by Shiliang Sun, et al. ∙ 0 ∙ share

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Nonlinear Kalman Filtering with Divergence Minimization

We consider the nonlinear Kalman filtering problem using Kullback-Leible...
05/01/2017 ∙ by San Gultekin, et al. ∙ 0 ∙ share

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Variational Inference via χ-Upper Bound Minimization

Variational inference (VI) is widely used as an efficient alternative to...
11/01/2016 ∙ by Adji B. Dieng, et al. ∙ 0 ∙ share

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

We introduce a deep network architecture called DerainNet for removing r...
09/07/2016 ∙ by Xueyang Fu, et al. ∙ 0 ∙ share

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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...
06/10/2015 ∙ by Aaron Schein, et al. ∙ 0 ∙ share

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Stochastic Annealing for Variational Inference

We empirically evaluate a stochastic annealing strategy for Bayesian pos...
05/25/2015 ∙ by San Gultekin, et al. ∙ 0 ∙ share

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A Collaborative Kalman Filter for Time-Evolving Dyadic Processes

We present the collaborative Kalman filter (CKF), a dynamic model for co...
01/22/2015 ∙ by San Gultekin, et al. ∙ 0 ∙ share

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A Nested HDP for Hierarchical Topic Models

We develop a nested hierarchical Dirichlet process (nHDP) for hierarchic...
01/16/2013 ∙ by John Paisley, et al. ∙ 0 ∙ share

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Nested Hierarchical Dirichlet Processes

We develop a nested hierarchical Dirichlet process (nHDP) for hierarchic...
10/25/2012 ∙ by John Paisley, et al. ∙ 0 ∙ share

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Stochastic Variational Inference

We develop stochastic variational inference, a scalable algorithm for ap...
06/29/2012 ∙ by Matt Hoffman, et al. ∙ 0 ∙ share

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Variational Bayesian Inference with Stochastic Search

Mean-field variational inference is a method for approximate Bayesian po...
06/27/2012 ∙ by John Paisley, et al. ∙ 0 ∙ share

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Combinatorial clustering and the beta negative binomial process

We develop a Bayesian nonparametric approach to a general family of late...
11/08/2011 ∙ by Tamara Broderick, et al. ∙ 0 ∙ share

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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 ...
09/02/2011 ∙ by John Paisley, et al. ∙ 0 ∙ share

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The Discrete Infinite Logistic Normal Distribution

We present the discrete infinite logistic normal distribution (DILN), a ...
03/24/2011 ∙ by John Paisley, et al. ∙ 0 ∙ share

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

In this paper, we propose TopicRNN, a recurrent neural network (RNN)-bas...
11/05/2016 ∙ by Adji B. Dieng, et al. ∙ 0 ∙ share

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Online Forecasting Matrix Factorization

In this paper the problem of forecasting high dimensional time series is...
12/23/2017 ∙ by San Gultekin, et al. ∙ 0 ∙ share

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A Deep Error Correction Network for Compressed Sensing MRI

Compressed sensing for magnetic resonance imaging (CS-MRI) exploits imag...
03/23/2018 ∙ by Liyan Sun, et al. ∙ 0 ∙ share

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A Divide-and-Conquer Approach to Compressed Sensing MRI

Compressed sensing (CS) theory assures us that we can accurately reconst...
03/27/2018 ∙ by Liyan Sun, et al. ∙ 0 ∙ share

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A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI

Compressed sensing MRI is a classic inverse problem in the field of comp...
04/04/2018 ∙ by Zhiwen Fan, et al. ∙ 0 ∙ share

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

In multi-contrast magnetic resonance imaging (MRI), compressed sensing t...
04/10/2018 ∙ by Liyan Sun, et al. ∙ 0 ∙ share

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

The need for fast acquisition and automatic analysis of MRI data is grow...
05/06/2018 ∙ by Liyan Sun, et al. ∙ 0 ∙ share

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Lightweight Pyramid Networks for Image Deraining

Existing deep convolutional neural networks have found major success in ...
05/16/2018 ∙ by Xueyang Fu, et al. ∙ 0 ∙ share

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MEnet: A Metric Expression Network for Salient Object Segmentation

Recent CNN-based saliency models have achieved great performance on publ...
05/15/2018 ∙ by Shulian Cai, et al. ∙ 0 ∙ share

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MBA: Mini-Batch AUC Optimization

Area under the receiver operating characteristics curve (AUC) is an impo...
05/29/2018 ∙ by San Gultekin, et al. ∙ 0 ∙ share

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Fully Supervised Speaker Diarization

In this paper, we propose a fully supervised speaker diarization approac...
10/10/2018 ∙ by Aonan Zhang, et al. ∙ 0 ∙ share

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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...
10/25/2018 ∙ by Liyan Sun, et al. ∙ 0 ∙ share

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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...
11/15/2018 ∙ by Ceena Modarres, et al. ∙ 0 ∙ share

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Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process

Ensemble learning is a mainstay in modern data science practice. Convent...
12/08/2018 ∙ by Jeremiah Zhe Liu, et al. ∙ 0 ∙ share

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Mixed Membership Recurrent Neural Networks

Models for sequential data such as the recurrent neural network (RNN) of...
12/23/2018 ∙ by Ghazal Fazelnia, et al. ∙ 0 ∙ share

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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...
02/06/2019 ∙ by Mark Ibrahim, et al. ∙ 0 ∙ share

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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...
04/09/2019 ∙ by Huangxing Lin, et al. ∙ 0 ∙ share

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Random Function Priors for Correlation Modeling

The likelihood model of many high dimensional data X_n can be expressed ...
05/09/2019 ∙ by Aonan Zhang, et al. ∙ 0 ∙ share

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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...
04/01/2019 ∙ by Jeremiah Zhe Liu, et al. ∙ 0 ∙ share

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Reweighted Expectation Maximization

Training deep generative models with maximum likelihood remains a challe...
06/13/2019 ∙ by Adji B. Dieng, et al. ∙ 0 ∙ share

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Accurate Uncertainty Estimation and Decomposition in Ensemble Learning

Ensemble learning is a standard approach to building machine learning sy...
11/11/2019 ∙ by Jeremiah Zhe Liu, et al. ∙ 0 ∙ share

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Learning Rate Dropout

The performance of a deep neural network is highly dependent on its trai...
11/30/2019 ∙ by Huangxing Lin, et al. ∙ 0 ∙ share

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Risk Bounds for Low Cost Bipartite Ranking

Bipartite ranking is an important supervised learning problem; however, ...
12/02/2019 ∙ by San Gultekin, et al. ∙ 0 ∙ share

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John Paisley

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