A framework unifies recent online RNN training algorithms along four axes and demonstrates performance clustering on synthetic tasks, indicating that gradient alignment is insufficient to explain success especially for stochastic methods.
Christopher Roth, Ingmar Kanitscheider, and Ila Fiete
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A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks
A framework unifies recent online RNN training algorithms along four axes and demonstrates performance clustering on synthetic tasks, indicating that gradient alignment is insufficient to explain success especially for stochastic methods.