On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks

Posted by klaurent Peking

Kavli Affiliate: Li Xin Li | First 5 Authors: Xiangrui Li, Xin Li, Deng Pan, Dongxiao Zhu, | Summary: Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision. When training data exhibit class imbalances, the class-wise reweighted version of logistic and softmax […]


Continue.. On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks

Incorporating electronic information into Machine Learning potential energy surfaces via approaching the ground-state electronic energy as a function of atom-based electronic populations

Posted by klaurent Berkeley

Kavli Affiliate: Kristin A. Persson | First 5 Authors: Xiaowei Xie, Kristin A. Persson, David W. Small, , | Summary: Machine Learning (ML) approximations to Density Functional Theory (DFT) potential energy surfaces (PESs) are showing great promise for reducing the computational cost of accurate molecular simulations, but at present they are not applicable to varying […]


Continue.. Incorporating electronic information into Machine Learning potential energy surfaces via approaching the ground-state electronic energy as a function of atom-based electronic populations