Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …

Big Data applications are typically associated with systems involving large numbers of
users, massive complex software systems, and large-scale heterogeneous computing and …

The conventional recipe for maximizing model accuracy is to (1) train multiple models with
various hyperparameters and (2) pick the individual model which performs best on a held …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

In this paper we describe a new mobile architecture, mbox {MobileNetV2}, that improves the
state of the art performance of mobile models on multiple tasks and benchmarks as well as …

Develo** neural network image classification models often requires significant
architecture engineering. In this paper, we study a method to learn the model architectures …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

Recent interest in complex and computationally expensive machine learning models with
many hyperparameters, such as automated machine learning (AutoML) frameworks and …

Performance of machine learning algorithms depends critically on identifying a good set of
hyperparameters. While recent approaches use Bayesian optimization to adaptively select …

Very deep convolutional networks with hundreds of layers have led to significant reductions
in error on competitive benchmarks. Although the unmatched expressiveness of the many …