4 citations · 12 across the 17 of their papers we have counts for
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stat.ML2020
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks
Matthew F Dixon
Time series modeling has entered an era of unprecedented growth in the size and complexity of data which require new modeling approaches. While many new general purpose machine lea…
stat.ML2019★ 4 cited
Deep Fundamental Factor Models
Matthew F. Dixon, Nicholas G. Polson
Deep fundamental factor models are developed to automatically capture non-linearity and interaction effects in factor modeling. Uncertainty quantification provides interpretability…
stat.ML2017
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…