Trautner et al. win Top 5 at DDDAS 2020

Saturday, October 10, 2020

Trautner, Margolis, and Ravela  won the "Top 5" recognition for the paper presented at the  DDDAS 2020 conference. This paper contains three fundamentally transformative components: 1) Informative Ensemble Learning provides an efficacious parallel distributed path to learning and can replace Backprop 2) Information Gain provides a framework for optimizing $\ell_0$ problems in a Greedy manner, which appears to converge faster than $\ell_1$ approximation. 3)  Equations can be learned from data to build stable hybrid dynamical systems. 

Please click for  Informative Neural Ensemble Kalman Learning paper

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