Hybrid QML models trained with classical DP-SGD retain higher accuracy than classical models under fixed privacy budgets on synthetic and image-classification tasks.
In: 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE), pp
2 Pith papers cite this work. Polarity classification is still indexing.
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Evolutionary optimization discovers developmental reward schedules that improve performance over extrinsic-only baselines on some MiniGrid tasks, with novelty emerging as the dominant early signal.
citing papers explorer
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Private training in quantum machine learning
Hybrid QML models trained with classical DP-SGD retain higher accuracy than classical models under fixed privacy budgets on synthetic and image-classification tasks.
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Evolutionary Discovery of Developmental Reward Schedules in Deep Reinforcement Learning
Evolutionary optimization discovers developmental reward schedules that improve performance over extrinsic-only baselines on some MiniGrid tasks, with novelty emerging as the dominant early signal.