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4 produkter
4 produkter
Del 13024 - Lecture Notes in Computer Science
Pattern Recognition
43rd DAGM German Conference, DAGM GCPR 2021, Bonn, Germany, September 28 – October 1, 2021, Proceedings
Häftad, Engelska, 2022
1 235 kr
Skickas inom 10-15 vardagar
This book constitutes the refereed proceedings of the 43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021, which was held during September 28 – October 1, 2021. The conference was planned to take place in Bonn, Germany, but changed to a virtual event due to the COVID-19 pandemic.The 46 papers presented in this volume were carefully reviewed and selected from 116 submissions. They were organized in topical sections as follows: machine learning and optimization; actions, events, and segmentation; generative models and multimodal data; labeling and self-supervised learning; applications; and 3D modelling and reconstruction.
E-bok
Engelska, 20221 530 kr
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This book constitutes the refereed proceedings of the 43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021, which was held during September 28 – October 1, 2021. The conference was planned to take place in Bonn, Germany, but changed to a virtual event due to the COVID-19 pandemic.
The 46 papers presented in this volume were carefully reviewed and selected from 116 submissions. They were organized in topical sections as follows: machine learning and optimization; actions, events, and segmentation; generative models and multimodal data; labeling and self-supervised learning; applications; and 3D modelling and reconstruction.
Inbunden, Engelska, 2025
565 kr
Skickas inom 10-15 vardagar
This open access book presents the concept of Informed Machine Learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These range from health care over manufacturing and material science to more advanced combinations with deep learning, say, in the form of physical informed neural networks. The book is intended for those interested in modern informed machine learning for a wide range of practical applications where the aspect of small data sets is a challenge.Machine Learning with small amounts of data? After the recent success of Artificial Intelligence based on training with massive amounts of data, this idea may sound exotic. However, it addresses crucial needs of practitioners in industry. While many industrial applications stand to benefit from the use of AI, the amounts of data needed by current learning paradigms are often hard to come by in industrial settings. As an alternative, learning methods and models are called for which integrate other sources of knowledge in order to compensate for the lack of data. This is where the principle of “Informed Machine Learning” comes into play.Informed Machine Learning combines purely data driven learning and knowledge-based techniques to learn from both data and knowledge. This has several advantages. It reduces the need for data, it often results in smaller, less complex and more robust models, and even makes machine learning applicable in settings where data is scarce. The kind of knowledge to be incorporated into learning processes can take many different forms, for example, differential equations, analytical models, simulation results, logical rules, knowledge graphs, or human feedback which makes the approach overall very powerful and widely applicable.
Inbunden, Engelska, 2025
565 kr
Skickas inom 10-15 vardagar
This open access book is meant as a textbook for Computer Science students who are looking for a gentle introduction to the world of quantum computing. More specifically, it is written for readers who have basic knowledge of Artificial Intelligence (AI) and Machine Learning (ML) and have a certain familiarity with search algorithms, optimization techniques, and neural networks. This is not because the authors are interested in Quantum AI or Quantum ML, but because they start from the basic premise that there exists a conceptual bridge between certain AI/ML models and certain quantum computing models. The purpose of this book is therefore 1) to revisit these AI/ML models and their applications, and 2) to build on this familiar foundation to segue into the study of quantum computing and its possible use cases. The presentation is technical but pragmatic and practice oriented. The authors cover theory to the necessary extent but largely proceed in an example-driven manner. Most of the examples are concerned with combinatorial optimization and consider problems that can be cast as quadratic unconstrained binary optimization problems.Numerous python/numpy/scipy codes support the mathematical discussion and demonstrate how to put theory into practice, accompanied by exercises for each chapter. Parts of the material were adopted from long running lectures on pattern recognition, on the foundations of quantum computing, and on quantum computing algorithms, which are taught by the authors in the Computer Science master’s program at the University of Bonn.