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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
This open access book focuses on robot introspection, which has a direct impact on physical human–robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous beha…
- Edition
- 1
- ISBN/ISSN
- 9789811562631
- Collation
- XVII, 137 hlm; ill., lamp.,
- Series Title
- -
- Call Number
- -
Machine Learning and Its Application to Reacting Flows : ML and Combustion
These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies a…
- Edition
- -
- ISBN/ISSN
- 978-3-031-16248-0
- Collation
- XI, 346
- Series Title
- Lecture Notes in Energy (LNEN, volume 44)
- Call Number
- -
Multivariate Statistical Machine Learning Methods for Genomic Prediction
Thanks to advances in digital technologies like electronic devices and networks, it is possible to automatize and digitalize many jobs, processes, and services, which are generating huge quantities of data. These “big data” are transmitted, collected, aggregated, and analyzed to deliver deep insights into processes and human behavior. For this reason, data are called the new oil, since …
- Edition
- -
- ISBN/ISSN
- 9783030890100
- Collation
- XXIV, 691
- Series Title
- -
- Call Number
- -
Manifold Learning : Model Reduction in Engineering
This Open Access book reviews recent theoretical and numerical developments in nonlinear model order reduction in continuum mechanics, being addressed to Master and PhD students, as well as to researchers, lecturers and instructors. The aim of the authors is to provide tools for a better understanding and implement reduced order models by using: physics-based models, synthetic data forecast by …
- Edition
- -
- ISBN/ISSN
- 978-3-031-52764-7
- Collation
- X, 107
- Series Title
- Manifold Learning : Model Reduction in Engineering
- Call Number
- 001 RYC m
Multiple-Aspect Analysis of Semantic Trajectories: First International Worksh…
This open access book constitutes the refereed post-conference proceedings of the First International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019, held in conjunction with the 19th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, in Würzburg, Germany, in September 2019. The 8 full papers presented were carefully rev…
- Edition
- 1
- ISBN/ISSN
- 9783030380816
- Collation
- IX, 133
- Series Title
- -
- Call Number
- -
Foundation Models for Natural Language Processing
This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documen…
- Edition
- 1
- ISBN/ISSN
- 978-3-031-23190-2
- Collation
- -
- Series Title
- Artificial Intelligence: Foundations, Theory, and Algorithms
- Call Number
- XVIII, 436
Integrating Data Science and Earth Science
This open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows.
- Edition
- -
- ISBN/ISSN
- 978-3-030-99546-1
- Collation
- -
- Series Title
- -
- Call Number
- -
xxAI - Beyond Explainable AI
Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and …
- Edition
- 1
- ISBN/ISSN
- -
- Collation
- -
- Series Title
- Lecture Notes in Computer Science
- Call Number
- X, 397
Machine learners :archaeology of a data practice
Machine learning - programming computers to learn from data - has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as oper…
- Edition
- -
- ISBN/ISSN
- 9780262342551
- Collation
- 1 online resource (xvi, 252 pages) :illustrations
- Series Title
- -
- Call Number
- -
Boosting: Foundations and Algorithms
A remarkably rich theory has evolved around boosting, with connections to a range of topics including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. …
- Edition
- -
- ISBN/ISSN
- 9780262301183
- Collation
- 1 online resource (xv, 526 pages) :illustrations
- Series Title
- -
- Call Number
- -
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