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The links between optimal control of dynamical systems and neural networks have proved beneficial both from a theoretical and from a practical point of view.
Design of ion-implanted mosfet’s with very small physical dimensions
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Optimal control and viscosity solutions of Hamilton-Jacobi-Bellman equations
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Nonlinear convergence analysis for the parareal algorithm
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Implications of historical trends in the electrical efficiency of computing
Koomey, J., Berard, S., Sanchez, M., and Wong, H · 2011
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Distributed optimization of deeply nested systems
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A proposal on machine learning via dynamical systems
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Optimization methods for large-scale machine learning
Bottou, L., Curtis, F. E., and Nocedal, J · 2018
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Neural ordinary differential equations
Chen, T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D · 2018
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Beyond backprop: Alternating minimization with co-activation memory
Choromanska, A., Kumaravel, S., Luss, R., Rish, I., Kingsbury, B., Tejwani, R., and Bouneffouf, D · 2018
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Layer-parallel training of deep residual neural networks
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Stable architectures for deep neural networks
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Maximum principle based algorithms for deep learning
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Automatic differentiation in machine learning: a survey
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Decoupled parallel backpropagation with convergence guarantee
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A proximal block coordinate descent algorithm for deep neural network training
Lau, T. T.-K., Zeng, J., Wu, B., and Yao, Y · 2018
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Block coordinate descent for deep learning: Unified convergence guarantees
Zeng, J., Lau, T. T.-K., Lin, S., and Yao, Y · 2018
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