# Neural Optimal Control for Representation Learning

@article{Chalvidal2020NeuralOC, title={Neural Optimal Control for Representation Learning}, author={Mathieu Chalvidal and Matthew Ricci and R. VanRullen and Thomas Serre}, journal={ArXiv}, year={2020}, volume={abs/2006.09545} }

The intriguing connections recently established between neural networks and dynamical systems have invited deep learning researchers to tap into the well-explored principles of differential calculus. Notably, the adjoint sensitivity method used in neural ordinary differential equations (Neural ODEs) has cast the training of neural networks as a control problem in which neural modules operate as continuous-time homeomorphic transformations of features. Typically, these methods optimize a single… Expand

#### One Citation

Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems

- Computer Science, Physics
- ArXiv
- 2020

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