Kernel methods have recently attracted resurgent interest, showing performance competitive
with deep neural networks in tasks such as speech recognition. The random Fourier features …

We investigate how to train kernel approximation methods that generalize well under a
memory budget. Building on recent theoretical work, we define a measure of kernel …

Over the past five years or so, deep learning methods have dramatically improved the state
of the art performance in a variety of domains, including speech recognition, computer …

We study the performance of kernel methods on the acoustic modeling task for automatic
speech recognition, and compare their performance to deep neural networks (DNNs). To …

Abstract Machine learning models are trending larger to attain state-of-the-art performance
across various application domains. These state-of-the-art models can consume large …