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 ...
Deep learning is formulated as a discrete-time optimal control problem. This allows one to characterize necessary conditions for optimality and develop ...
People also ask
What is the optimization method used in deep learning?
Gradient Descent can be considered the popular kid among the class of optimizers in deep learning. This optimization algorithm uses calculus to consistently modify the values and achieve the local minimum. Before moving ahead, you might question what a gradient is.
Which optimization technique is the most commonly used for neural network training?
The most common type of optimization used in neural networks is gradient descent. This method involves repeatedly adjusting the values of the network's parameters until the performance improves. Different types of problems can be optimized in different ways and by using different methods.
What is optimal control in machine learning?
Deep Reinforcement Learning: the policy is approximated with a neural network. Optimal Control provides the best sequence of actions to take given some initial conditions and a model of how the system evolves through time.
What is LSTM in deep learning?
What is LSTM? LSTMs Long Short-Term Memory is a type of RNNs Recurrent Neural Network that can detain long-term dependencies in sequential data. LSTMs are able to process and analyze sequential data, such as time series, text, and speech.
Duration: 12:17
Posted: Sep 2, 2018
Posted: Sep 2, 2018
“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 ...
"An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks". The 35th International Conference on Machine Learning, 2018.
Optimization: Deep learning is formulated as a discrete-time optimal control problem. Our research bridging optimal control and optimization of deep neural ...
An optimal control approach to deep learning and applications to discrete-weight neural networks. In International Conference on Machine Learning, pages 2991–.