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We introduce MADGRAD, a novel optimization method in the family of AdaGradadaptive gradient methods. MADGRAD shows excellent performance on deep learningoptimization problems from multiple fields, including classification andimage-to-image tasks in vision, and recurrent and bidirectionally-masked modelsin natural language processing. For each of these tasks, MADGRAD matches oroutperforms both SGD and ADAM in test set performance, even on problems forwhich adaptive methods normally perform poorly.

Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization