30 citations · 119 across the 39 of their papers we have counts for
7 papers · 1 filter
Bayesian neural networks and dimensionality reduction
Deborshee Sen, Theodore Papamarkou, David Dunson
In conducting non-linear dimensionality reduction and feature learning, it is common to suppose that the data lie near a lower-dimensional manifold. A class of model-based approach…
Fiedler Regularization: Learning Neural Networks with Graph Sparsity
Edric Tam, David Dunson
We introduce a novel regularization approach for deep learning that incorporates and respects the underlying graphical structure of the neural network. Existing regularization meth…
Bayesian Distance Clustering
Leo L Duan, David B Dunson
Model-based clustering is widely-used in a variety of application areas. However, fundamental concerns remain about robustness. In particular, results can be sensitive to the choic…
Intrinsic Gaussian processes on complex constrained domains
Mu Niu, Pokman Cheung, Lizhen Lin +3
We propose a class of intrinsic Gaussian processes (in-GPs) for interpolation, regression and classification on manifolds with a primary focus on complex constrained domains or irr…
Bayesian Multi Plate High Throughput Screening of Compounds
Ivo D. Shterev, David B. Dunson, Cliburn Chan +1
High throughput screening of compounds (chemicals) is an essential part of drug discovery [7], involving thousands to millions of compounds, with the purpose of identifying candida…
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process
Ye Wang, David B. Dunson
Learning of low dimensional structure in multidimensional data is a canonical problem in machine learning. One common approach is to suppose that the observed data are close to a l…