Search Results for author: Kevin Luk

Found 5 papers, 0 papers with code

An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise

no code implementations21 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.

Stochastic Optimization

Scalable Recommender Systemsthrough Recursive Evidence Chains

no code implementations20 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.

Matrix Completion Recommendation Systems

Exploring Curvature Noise in Large-Batch Stochastic Optimization

no code implementations27 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.

Stochastic Optimization

A Coordinate-Free Construction of Scalable Natural Gradient

no code implementations30 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.

Scalable Recommender Systems through Recursive Evidence Chains

no code implementations5 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.

Matrix Completion Recommendation Systems

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