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Found 3 papers, 1 papers with code
The case for model-driven interpretability of delay-based congestion control protocols
Analyzing and interpreting the exact behavior of new delay-based congestion control protocols with complex non-linear control loops is exceptionally difficult in highly variable networks such as cellular networks.
Networking and Internet Architecture Systems and Control Systems and Control
Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients
While neuroevolution (evolving neural networks) has a successful track record across a variety of domains from reinforcement learning to artificial life, it is rarely applied to large, deep neural networks.
ES Is More Than Just a Traditional Finite-Difference Approximator
However, this ES optimizes for a different gradient than just reward: It optimizes for the average reward of the entire population, thereby seeking parameters that are robust to perturbation.
