986 citations · 2k across the 50 of their papers we have counts for
50 papers
Bayesian Gaussian Process ODEs via Double Normalizing Flows
Jian Xu, Shian Du, Junmei Yang +3
Recently, Gaussian processes have been used to model the vector field of continuous dynamical systems, referred to as GPODEs, which are characterized by a probabilistic ODE equatio…
Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks
Arunesh Mittal, Kai Yang, Paul Sajda +1
Several approximate inference methods have been proposed for deep discrete latent variable models. However, non-parametric methods which have previously been successfully employed…
Nonlinear Kalman Filtering with Reparametrization Gradients
San Gultekin, Brendan Kitts, Aaron Flores +1
We introduce a novel nonlinear Kalman filter that utilizes reparametrization gradients. The widely used parametric approximation is based on a jointly Gaussian assumption of the st…
Self-Verification in Image Denoising
Huangxing Lin, Yihong Zhuang, Delu Zeng +3
We devise a new regularization, called self-verification, for image denoising. This regularization is formulated using a deep image prior learned by the network, rather than a trad…
Bayesian non-parametric non-negative matrix factorization for pattern identification in environmental mixtures
Elizabeth A. Gibson, Sebastian T. Rowland, Jeff Goldsmith +3
Environmental health researchers may aim to identify exposure patterns that represent sources, product use, or behaviors that give rise to mixtures of potentially harmful environme…
Scalable Community Detection in Massive Networks Using Aggregated Relational Data
Timothy Jones, Owen G. Ward, Yiran Jiang +2
The mixed membership stochastic blockmodel (MMSB) is a popular Bayesian network model for community detection. Fitting such large Bayesian network models quickly becomes computatio…
Adaptive noise imitation for image denoising
Huangxing Lin, Yihong Zhuang, Yue Huang +4
The effectiveness of existing denoising algorithms typically relies on accurate pre-defined noise statistics or plenty of paired data, which limits their practicality. In this work…
Bayesian recurrent state space model for rs-fMRI
Arunesh Mittal, Scott Linderman, John Paisley +1
We propose a hierarchical Bayesian recurrent state space model for modeling switching network connectivity in resting state fMRI data. Our model allows us to uncover shared network…
Deep Bayesian Nonparametric Factor Analysis
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. We outline a stochastic E…
Learning Rate Dropout
Huangxing Lin, Weihong Zeng, Xinghao Ding +3
The performance of a deep neural network is highly dependent on its training, and finding better local optimal solutions is the goal of many optimization algorithms. However, exist…
Risk Bounds for Low Cost Bipartite Ranking
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. To circumvent t…
Noise2Blur: Online Noise Extraction and Denoising
Huangxing Lin, Weihong Zeng, Xinghao Ding +3
We propose a new framework called Noise2Blur (N2B) for training robust image denoising models without pre-collected paired noisy/clean images. The training of the model requires on…
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou +1
Ensemble learning is a standard approach to building machine learning systems that capture complex phenomena in real-world data. An important aspect of these systems is the complet…
Reweighted Expectation Maximization
Adji B. Dieng, John Paisley
Training deep generative models with maximum likelihood remains a challenge. The typical workaround is to use variational inference (VI) and maximize a lower bound to the log margi…
Random Function Priors for Correlation Modeling
Aonan Zhang, John Paisley
The likelihood model of high dimensional data can often be expressed as , where is a collection of hidden features shared…
Rain O'er Me: Synthesizing real rain to derain with data distillation
Huangxing Lin, Yanlong Li, Xinghao Ding +3
We present a supervised technique for learning to remove rain from images without using synthetic rain software. The method is based on a two-stage data distillation approach: 1) A…
Spatially Adaptive Ensemble Learning with Calibrated Predictive Uncertainty
Yanran Li, Jeremiah Zhe Liu, John Paisley +2
Air pollution exposure assessment often uses ensembles of spatio-temporal models, but a critical limitation is that conventional methods use deterministic weights and fail to quant…
Global Explanations of Neural Networks: Mapping the Landscape of Predictions
Mark Ibrahim, Melissa Louie, Ceena Modarres +1
A barrier to the wider adoption of neural networks is their lack of interpretability. While local explanation methods exist for one prediction, most global attributions still reduc…
Mixed Membership Recurrent Neural Networks
Ghazal Fazelnia, Mark Ibrahim, Ceena Modarres +2
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…
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process
Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou +1
Ensemble learning is a mainstay in modern data science practice. Conventional ensemble algorithms assigns to base models a set of deterministic, constant model weights that (1) do…
Towards Explainable Deep Learning for Credit Lending: A Case Study
Ceena Modarres, Mark Ibrahim, Melissa Louie +1
Deep learning adoption in the financial services industry has been limited due to a lack of model interpretability. However, several techniques have been proposed to explain predic…
A Deep Tree-Structured Fusion Model for Single Image Deraining
Xueyang Fu, Qi Qi, Yue Huang +3
We propose a simple yet effective deep tree-structured fusion model based on feature aggregation for the deraining problem. We argue that by effectively aggregating features, a rel…
An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection
Liyan Sun, Jiexiang Wang, Yue Huang +3
The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervise…
Fully Supervised Speaker Diarization
Aonan Zhang, Quan Wang, Zhenyao Zhu +2
In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker-discrimina…