Inspired by progress in unsupervised representation learning for natural language, we
examine whether similar models can learn useful representations for images. We train a …

With widespread advances in machine learning, a number of large enterprises are
beginning to incorporate machine learning models across a number of products. These …

Generalization performance of classifiers in deep learning has recently become a subject of
intense study. Deep models, which are typically heavily over-parametrized, tend to fit the …

GPUs are the workhorse in modern server infrastructure fueling advances in a number of
compute-intensive workloads such as deep neural network (DNN) training. Several recent …

Intrusion detection and network traffic classification are two of the main research
applications of machine learning to highly demanding data networks eg IoT/sensors …

With widespread advances in machine learning, a number of large enterprises are
beginning to incorporate machine learning models across a number of products. These …

Remarkable recent success of deep neural networks has not been easy to analyze
theoretically. It has been particularly hard to disentangle relative significance of architecture …

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

Noisy intermediate-scale quantum computing devices are an exciting platform for the
exploration of the power of near-term quantum applications. Performing nontrivial tasks in …

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 …