By Most work on neural architecture search (NAS, see our recent survey) solely optimizes for one criterion: high performance (measured in terms of accuracy). This often results in large and complex network architectures that cannot be used in real-world applications with several other important criteria including memory requirement, energy consumption and latency. The other problem … Continue reading LEMONADE: Efficient Multi-objective Neural Architecture Search with Network Morphisms