no code implementations • 15 Mar 2022 • Elizabeth Coda, Nico Courts, Colby Wight, Loc Truong, Woongjo Choi, Charles Godfrey, Tegan Emerson, Keerti Kappagantula, Henry Kvinge
That is, a single input can potentially yield many different outputs (whether due to noise, imperfect measurement, or intrinsic stochasticity in the process) and many different inputs can yield the same output (that is, the map is not injective).
no code implementations • 3 Dec 2021 • Loc Truong, Woongjo Choi, Colby Wight, Lizzy Coda, Tegan Emerson, Keerti Kappagantula, Henry Kvinge
We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.
no code implementations • 9 Jul 2021 • Henry Kvinge, Colby Wight, Sarah Akers, Scott Howland, Woongjo Choi, Xiaolong Ma, Luke Gosink, Elizabeth Jurrus, Keerti Kappagantula, Tegan H. Emerson
As both machine learning models and the datasets on which they are evaluated have grown in size and complexity, the practice of using a few summary statistics to understand model performance has become increasingly problematic.
no code implementations • 6 Jan 2021 • Elliott Skomski, Soumya Vasisht, Colby Wight, Aaron Tuor, Jan Drgona, Draguna Vrabie
Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics.