Deep reinforcement learning (RL) has become one of the most popular topics in artificial
intelligence research. It has been widely used in various fields, such as end-to-end control …

Cloud computing has rapidly emerged as a model for delivering Internet-based utility
computing services. Infrastructure as a Service (IaaS) is one of the most important and …

In the last few years, graph neural networks (GNNs) have become the standard toolkit for
analyzing and learning from data on graphs. This emerging field has witnessed an extensive …

Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …

Query optimization is one of the most challenging problems in database systems. Despite
the progress made over the past decades, query optimizers remain extremely complex …

Recent efforts applying machine learning techniques to query optimization have shown few
practical gains due to substantive training overhead, inability to adapt to changes, and poor …

Specialized accelerators such as GPUs, TPUs, FPGAs, and custom ASICs have been
increasingly deployed to train deep learning models. These accelerators exhibit …

The conference papers and full proceedings are available to registered attendees now and
will be available to everyone beginning Wednesday, July 14, 2021. Paper abstracts and …

Cloud applications are increasingly shifting from large monolithic services, to large numbers
of loosely-coupled, specialized microservices. Despite their advantages in terms of …

End-to-end training of neural network solvers for graph combinatorial optimization problems
such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently …