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An Empirical Exploration of Recurrent Network Architectures.

, , and . ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 2342-2350. JMLR.org, (2015)

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Multi-task sequence to sequence learning, , , , and . arXiv preprint arXiv:1511.06114, (2015)An Empirical Exploration of Recurrent Network Architectures., , and . ICML, volume 37 of JMLR Workshop and Conference Proceedings, page 2342-2350. JMLR.org, (2015)On the importance of initialization and momentum in deep learning., , , and . ICML (3), volume 28 of JMLR Workshop and Conference Proceedings, page 1139-1147. JMLR.org, (2013)Neural Programmer: Inducing Latent Programs with Gradient Descent., , and . ICLR (Poster), (2016)Dota 2 with Large Scale Deep Reinforcement Learning., , , , , , , , , and 15 other author(s). CoRR, (2019)An Empirical Exploration of Recurrent Network Architectures, , and . ICML, (2015)Language models are unsupervised multitask learners, , , , , and . (2019)Modelling Relational Data using Bayesian Clustered Tensor Factorization., , and . NIPS, page 1821-1828. Curran Associates, Inc., (2009)FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models, , , , and . (2018)cite arxiv:1810.01367Comment: 8 Pages, 6 figures.Deep Double Descent: Where Bigger Models and More Data Hurt, , , , , and . (2019)cite arxiv:1912.02292Comment: G.K. and Y.B. contributed equally.