To facilitate the co-design of next generation hardware architectures, it is critical to
characterize the workloads of deep learning (DL) applications and assess their …

Deep learning for time series is an emerging area with close ties to industry, yet under
represented in performance benchmarks for machine learning systems. In this paper, we …

A major bottleneck of the current Machine Learning (ML) workflow is the time consuming,
error prone engineering required to get data from a datastore or a database (DB) to the point …

We present PyTorch Geometric Temporal, a deep learning framework combining state-of-the-
art machine learning algorithms for neural spatiotemporal signal processing. The main goal …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

Time series data is ubiquitous in various domains, making it crucial for numerous research
and practical applications. Previous methods have primarily focused on modeling local …

Inference on time series data is a common requirement in many scientific disciplines and
internet of things (IoT) applications, yet there are few resources available to domain …

Internet services supported by large-scale distributed systems have become essential for
our daily life. To ensure the stability and high quality of services, diverse metric data are …

Data scale has been acknowledged as a crucial factor for enhancing the generalization and
effectiveness of pre-training models. While existing methods of multivariate time series pre …

Intelligent monitoring and control of critical infrastructure such as electric power grids, public
water utilities and transportation systems produces massive volumes of time series data from …