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tinyML Talks Webcast - recorded March 10, 2021 “Efficient Multi-Objective Neural Architecture Search with Evolutionary Algorithms“ Thomas Elsken Bosch Center for Artificial Intelligence Deep Learning has enabled remarkable progress over the last years on a variety of tasks such as image recognition. One crucial aspect for this progress are novel neural architectures. Currently employed architectures have mostly been developed manually by human experts, which is a time-consuming and error-prone process. This led to a growing interest in neural architecture search (NAS), the process of automatically finding neural network architectures for a task at hand. While recent approaches have achieved state-of-the-art predictive performance, they are problematic under resource constraints for two reasons: (1) the neural architectures found are typically solely optimized for high predictive performance, without penalizing excessive resource consumption; (2) most architecture search methods requ
