1k citations · 1.3k across the 26 of their papers we have counts for
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Quantum Bayesian Computation
Nick Polson, Vadim Sokolov, Jianeng Xu
Quantum Bayesian Computation (QBC) is an emerging field that levers the computational gains available from quantum computers to provide an exponential speed-up in Bayesian computat…
Data Augmentation for Bayesian Deep Learning
Yuexi Wang, Nicholas G. Polson, Vadim O. Sokolov
Deep Learning (DL) methods have emerged as one of the most powerful tools for functional approximation and prediction. While the representation properties of DL have been well stud…
Deep Learning
Nicholas G. Polson, Vadim O. Sokolov
Deep learning (DL) is a high dimensional data reduction technique for constructing high-dimensional predictors in input-output models. DL is a form of machine learning that uses hi…
Deep Learning for Energy Markets
Michael Polson, Vadim Sokolov
Deep Learning is applied to energy markets to predict extreme loads observed in energy grids. Forecasting energy loads and prices is challenging due to sharp peaks and troughs that…
Deep Reinforcement Learning for Dynamic Urban Transportation Problems
Laura Schultz, Vadim Sokolov
We explore the use of deep learning and deep reinforcement learning for optimization problems in transportation. Many transportation system analysis tasks are formulated as an opti…
Deep Learning: A Bayesian Perspective
Nicholas Polson, Vadim Sokolov
Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction. By taking a Bayesian probabilistic perspective, we provide a number of i…
Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading
Matthew F. Dixon, Nicholas G. Polson, Vadim O. Sokolov
Deep learning applies hierarchical layers of hidden variables to construct nonlinear high dimensional predictors. Our goal is to develop and train deep learning architectures for s…