765 citations · 773 across the 21 of their papers we have counts for
Showing 2018 · stat.MLShow all
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stat.ML2018
Reconciling modern machine learning practice and the bias-variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma +1
Breakthroughs in machine learning are rapidly changing science and society, yet our fundamental understanding of this technology has lagged far behind. Indeed, one of the central t…
stat.ML2018
Kernel machines that adapt to GPUs for effective large batch training
Siyuan Ma, Mikhail Belkin
Modern machine learning models are typically trained using Stochastic Gradient Descent (SGD) on massively parallel computing resources such as GPUs. Increasing mini-batch size is a…
stat.ML2018
To understand deep learning we need to understand kernel learning
Mikhail Belkin, Siyuan Ma, Soumik Mandal
Generalization performance of classifiers in deep learning has recently become a subject of intense study. Deep models, typically over-parametrized, tend to fit the training data e…