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数据分析与问题求解讨论班
数据分析与问题求解讨论班
Adjoint Gradient Computation for Initial State and Network Parameters in Neural ODE Method
Adjoint Gradient Computation for Initial State and Network Parameters in Neural ODE Method
组织者
演讲者
时间
2026年05月15日 15:00 至 16:00
地点
A3-1-301
线上
Zoom 204 323 0165
(BIMSA)
摘要
The training of Neural ODEs requires the estimations of the initial state as well as the network parameters. This talk introduces the adjoint gradient method for their computations. I will first describe the Neural ODE method for the state evolution in continuous time, and then the adjoint gradient method. A one-dimensional example is presented to illustrate how adjoint variables are propagated backward when losses exist at multiple observation times, and how the initial state and model parameters are optimized accordingly.
演讲者介绍
Zhang Qian is a second-year Ph.D. student in a joint Program between BIMSA and Renmin University of China, majoring in Statistics and Big Data, under the supervision of Professor Zhang Xiaoming. Her current research focuses on the data-driven discovery of differential equations from noisy and irregularly sampled time-series data using machine learning techniques.