The talk titled "Neural Ordinary Differential Equations" provides a review of a new family of deep neural network models called Neural Ordinary Differential Equations (NODEs). The NODEs model, developed by Chen et al., parameterizes the hidden state of a neural network. This allows the model to capture long-term dependencies and adapt its behavior based on input data. The talk covers the key features of NODEs and their potential applications in various fields. It also discusses the challenges and limitations of the NODEs model and its future development.
