no code implementations • 21 Feb 2019 • Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan, Jimmy Ba
We demonstrate that the learning performance of our method is more accurately captured by the structure of the covariance matrix of the noise rather than by the variance of gradients.
no code implementations • 20 Oct 2018 • Elias Tragas, Calvin Luo, Maxime Yvez, Kevin Luk, David Duvenaud
A popular matrix completion algorithm is matrix factorization, where ratings are predicted from combining learned user and item parameter vectors.
no code implementations • 27 Sep 2018 • Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan, Jimmy Ba
Unfortunately, a major drawback is the so-called generalization gap: large-batch training typically leads to a degradation in generalization performance of the model as compared to small-batch training.
no code implementations • 30 Aug 2018 • Kevin Luk, Roger Grosse
Most neural networks are trained using first-order optimization methods, which are sensitive to the parameterization of the model.
no code implementations • 5 Jul 2018 • Elias Tragas, Calvin Luo, Maxime Gazeau, Kevin Luk, David Duvenaud
Recommender systems can be formulated as a matrix completion problem, predicting ratings from user and item parameter vectors.