Jan 4, 2024 · Abstract. We present a neural network approach for approximating the value function of high- dimensional stochastic control problems.

Aug 15, 2023 · A Mean-Field Optimal Control Formulation of Deep Learning. ... An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks.

Sep 14, 2023 · An optimal control approach to deep learning and applications to discrete- weight neural networks. In International Conference on Machine Learning, 2985 ...

Jun 22, 2024 · Deep Learning, Residual Neural Networks, Optimal control, Stability. 1 ... An optimal control approach to deep learning and applications to discrete-weight.

Nov 14, 2023 · Abstract: NeurODEs are a special type of neural networks that incorporate shortcut connections, enabling their training to be interpreted as an optimal control ...

6 days ago · We propose and analyze a numerical algorithm for solving a class of optimal control problems for learning-informed semilinear partial differential equations ...

May 31, 2024 · [24] A. Bemporad, “A piecewise linear regression and classification algorithm with application to learning and model predictive control of hybrid systems,” IEEE ...

Aug 15, 2023 · The proposed framework leads to optimal adaptive time discretiza- tions of DNNs, such as ResNets and Fractional-DNNs, tailored to the optimization/learning ...

Jun 16, 2024 · Professor Bertsekas' teaching and research have spanned several fields, including deterministic optimization, dynamic programming and stochastic control, large- ...

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