no code implementations • 14 Dec 2020 • Yiding Jiang, Pierre Foret, Scott Yak, Daniel M. Roy, Hossein Mobahi, Gintare Karolina Dziugaite, Samy Bengio, Suriya Gunasekar, Isabelle Guyon, Behnam Neyshabur
Understanding generalization in deep learning is arguably one of the most important questions in deep learning.
In today's heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability.
Ranked #1 on Fine-Grained Image Classification on Food-101 (using extra training data)
We present a probabilistic framework for multilingual neural machine translation that encompasses supervised and unsupervised setups, focusing on unsupervised translation.