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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 2 covers Machine Learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, Machine Learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by Machine Learning.
Artificial Intelligence (AI) began as a model of cognitive processes. This applies to several levels: the neurons at the subsymbolic level, models of learning and decision-making at the individual level, and the process of scientific discovery at the epistemological level, which is our concern here. As a model of the scientific process, Machine Learning started early on to discover regularities in data, generate hypotheses, and test them. Statistics is employed for hypothesis testing, but Machine Learning has a much broader view of the overall process of modeling, including hypothesis generation, self-supervision of the learning algorithm, creating representations, and the generation of new data by conducting experiments automatically. Approaches to enhance given or learned models, called “explanation-based learning” were also discovered as part of the field.
Contents:
Introduction
Challenges in Particle and Astroparticle Physics
Key Concepts in Machine Learning and Data Analysis
Data Acquisition and Data Structure
Monte Carlo Simulations
Data Storage and Access
Monitoring and Feature Extraction
Event Property Estimation and Signal Background Separation
Deep Learning Applications
Inverse Problems
Bibliography
Index