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A mean-field optimal control formulation of deep learning

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Abstract

Recent work linking deep neural networks and dynamical systems opened up new avenues to analyze deep learning. In particular, it is observed that new insights can be obtained by recasting deep learning as an optimal control problem on difference or differential equations. However, the mathematical aspects of such a formulation have not been systematically explored. This paper introduces the mathematical formulation of the population risk minimization problem in deep learning as a mean-field optimal control problem. Mirroring the development of classical optimal control, we state and prove optimality conditions of both the Hamilton–Jacobi–Bellman type and the Pontryagin type. These mean-field results reflect the probabilistic nature of the learning problem. In addition, by appealing to the mean-field Pontryagin’s maximum principle, we establish some quantitative relationships between population and empirical learning problems. This serves to establish a mathematical foundation for investigating the algorithmic and theoretical connections between optimal control and deep learning.

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Acknowledgements

The work of W. E and J. Han is supported in part by ONR Grant N00014-13-1-0338 and Major Program of NNSFC under Grant 91130005. Q. Li is supported by the Agency for Science, Technology and Research, Singapore.

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Authors and Affiliations

  1. Princeton University, Princeton, NJ, 08544, USA

    Weinan E & Jiequn Han

  2. Beijing Institute of Big Data Research and Peking University, Beijing, 100871, China

    Weinan E

  3. Institute of High Performance Computing, Agency for Science, Technology and Research, 1 Fusionopolis Way, Connexis North, Singapore, 138632, Singapore

    Qianxiao Li

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  1. Weinan E
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Correspondence to Jiequn Han.

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E, W., Han, J. & Li, Q. A mean-field optimal control formulation of deep learning. Res Math Sci 6, 10 (2019). https://doi.org/10.1007/s40687-018-0172-y

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