1 code implementation • 22 Sep 2023 • John S. Schreck, David John Gagne II, Charlie Becker, William E. Chapman, Kim Elmore, Da Fan, Gabrielle Gantos, Eliot Kim, Dhamma Kimpara, Thomas Martin, Maria J. Molina, Vanessa M. Pryzbylo, Jacob Radford, Belen Saavedra, Justin Willson, Christopher Wirz
In order to encourage broader adoption of evidential deep learning in Earth System Science, we have developed a new Python package, MILES-GUESS (https://github. com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning.
no code implementations • 5 Dec 2018 • Haipeng Jia, Xueshuang Xiang, Da Fan, Meiyu Huang, Changhao Sun, Yang He
Addressing these two issues, this paper proposes the Drop Pruning approach, which leverages stochastic optimization in the pruning process by introducing a drop strategy at each pruning step, namely, drop away, which stochastically deletes some unimportant weights, and drop back, which stochastically recovers some pruned weights.