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Finite-time Lyapunov exponents of deep neural networks
We compute how small input perturbations affect the output of deep neural networks, exploring an analogy between deep networks and dynamical systems, where the growth or decay of local perturbations is characterised by finite-time Lyapunov exponents.
Constraints on parameter choices for successful reservoir computing
By selecting network parameters such that the dynamics of the network is contractive, characterized by a negative maximal Lyapunov exponent, the network may synchronize with the driving signal.
Finding Efficient Swimming Strategies in a Three Dimensional Chaotic Flow by Reinforcement Learning
We apply a reinforcement learning algorithm to show how smart particles can learn approximately optimal strategies to navigate in complex flows.
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