Tweeted By @RogerGrosse

Reversible RNNs: reduce memory costs of GRU and LSTM networks by 10-15x without loss in performance. Also 5-10x for attention-based architectures. New paper with Matt MacKay, Paul Vicol, and Jimmy Ba, to appear at NIPS. https://t.co/Bx2v6mOn7O

— Roger Grosse (@RogerGrosse) October 26, 2018