演讲者：Yanzhao Cao（奥本大学）
时间：2020-12-14 10:00-11:00
地点：腾讯会议 ID 815 482 217
Abstract
I will first give a mathematical introduction to deep learning. Then I will talk about recent work on uncertainty quantification (UQ) of deep learning. In our UQ for deep neural networks (DNN) framework, the DNN architecture is the neural-ODE which formulates the evolution of potentially huge hidden layers in the DNN as a discretized ODE system. To characterize the randomness caused by the uncertainty of models and noises of data, we add a multiplicative Brownian motion noise to the ODE as a stochastic diffusion term, where the drift parameters serve as the prediction of the network, and the stochastic diffusion governs the randomness of network output.
