1.5k citations · 2.8k across the 17 of their papers we have counts for
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Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti +3
Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop-based algo…
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such +3
Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challeng…
Robust Spatial Filtering with Graph Convolutional Neural Networks
Felipe Petroski Such, Shagan Sah, Miguel Dominguez +5
Convolutional Neural Networks (CNNs) have recently led to incredible breakthroughs on a variety of pattern recognition problems. Banks of finite impulse response filters are learne…