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Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel Machine Learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to Machine Learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering.
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several Machine Learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.
The Weisfeiler-Leman method is a classic heuristic for graph isomorphism testing, which iteratively encodes vertex neighborhoods of increasing radius by vertex colors. Two graphs whose vertex colors do not match are called non-isomorphic. The method is fundamental for recent advances in machine learning with graphs, e.g., graph kernels and graph neural networks. This contribution overviews the development of graph kernels based on the Weisfeiler-Leman algorithm, which are among the most successful graph kernels today. We describe the Weisfeiler-Leman heuristic for graph isomorphism testing, from which the classical Weisfeiler-Leman subtree kernel directly follows. Further, we summarize the theory of optimal assignment kernels and present the Weisfeiler-Leman optimal assignment kernel for graphs and the related Wasserstein Weisfeiler-Leman graph kernel. We discuss kernel functions based on the k-dimensional Weisfeiler-Leman algorithm, a strict generalization of the Weisfeiler-Leman heuristic. We show that a local, sparsity-aware variant of this algorithm can lead to scalable and expressive kernels. Moreover, we survey other kernels based on the principle of Weisfeiler-Leman refinement. Finally, we shed some light on the connection between Weisfeiler-Leman-based kernels and neural architectures for graph-structured input.
Contents:
Preface
Introduction
Data Gathering and Resource Measuring
Streaming Data, Small Devices
Structured Data
Cluster Analysis
Hardware-Aware Execution
Memory Awareness
Communication Awareness
Energy Awareness
Bibliography
Index