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Neural Estimation and Optimization of Directed Information over Continuous Spaces
[article]
2022
arXiv
pre-print
This work develops a new method for estimating and optimizing the directed information rate between two jointly stationary and ergodic stochastic processes. Building upon recent advances in machine learning, we propose a recurrent neural network (RNN)-based estimator which is optimized via gradient ascent over the RNN parameters. The estimator does not require prior knowledge of the underlying joint and marginal distributions. The estimator is also readily optimized over continuous input
arXiv:2203.14743v1
fatcat:irj45fufdjdhxnarqzkoluxz3a