Mar 4, 2018 · Abstract:Deep learning is formulated as a discrete-time optimal control problem. This allows one to characterize necessary conditions for ...
The developed methods are applied to train, in a rather principled way, neural networks with weights that are constrained to take values in a discrete set.
The discrete-time method of successive approximations (MSA), which is based on the Pontryagin's maximum principle, is introduced for training neural ...
Jun 2, 2018 · Deep learning is formulated as a discrete-time optimal control problem. This allows one to char- acterize necessary conditions for ...
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Deep learning is formulated as a discrete-time optimal control problem. This allows one to characterize necessary conditions for optimality and develop ...
"An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks". The 35th International Conference on Machine Learning, 2018.
An optimal control approach to deep learning and applications to discrete-weight neural networks. In International Conference on Machine Learning, pages ...
“An Optimal Control Approach to Deep Learning and. Applications to Discrete-Weight Neural Networks”. A Full Statement and Sketch of the Proof of Theorem 1. In ...
Duration: 12:17
Posted: Sep 2, 2018
Posted: Sep 2, 2018
Feb 3, 2023 · The main contribution of this paper is to propose a new multilayer neural network training algorithm based on OC theory by modeling adaptable ...