default search action
Artificial Intelligence: Foundations, Theory, and Algorithms
2023
- Xiaowei Huang, Gaojie Jin, Wenjie Ruan:
Machine Learning Safety. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2023, ISBN 978-981-19-6813-6, pp. 3-263 - Qionghai Dai, Yue Gao:
Hypergraph Computation. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2023, ISBN 978-981-99-0184-5, pp. 1-244 - Gerhard Paaß, Sven Giesselbach
:
Foundation Models for Natural Language Processing - Pre-trained Language Models Integrating Media. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2023, ISBN 978-3-031-23189-6, pp. 1-419 - Xu Tan:
Neural Text-to-Speech Synthesis. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2023, ISBN 978-981-99-0826-4, pp. 1-185
2022
- Chuan Shi, Xiao Wang, Philip S. Yu:
Heterogeneous Graph Representation Learning and Applications. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2022, ISBN 978-981-16-6165-5, pp. 1-318
2021
- Fabio Cuzzolin:
The Geometry of Uncertainty - The Geometry of Imprecise Probabilities. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2021, ISBN 978-3-030-63152-9, pp. 1-728
2019
- Virginia Dignum:
Responsible Artificial Intelligence - How to Develop and Use AI in a Responsible Way. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2019, ISBN 978-3-030-30370-9, pp. 1-120
2017
- Paula Boddington
Towards a Code of Ethics for Artificial Intelligence. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2017, ISBN 978-3-319-60647-7, pp. 1-111
2016
- Stefano Mariani:
Coordination of Complex Sociotechnical Systems - Self-organisation of Knowledge in MoK. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2016, ISBN 978-3-319-47108-2, pp. 1-234 - Christian Blum, Günther R. Raidl:
Hybrid Metaheuristics - Powerful Tools for Optimization. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2016, ISBN 978-3-319-30882-1, pp. 1-136 - David Bergman, André A. Ciré, Willem-Jan van Hoeve, John N. Hooker:
Decision Diagrams for Optimization. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2016, ISBN 978-3-319-42847-5, pp. 1-234 - Audun Jøsang
Subjective Logic - A Formalism for Reasoning Under Uncertainty. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2016, ISBN 978-3-319-42335-7, pp. 1-326
2015
- Justyna Petke
Bridging Constraint Satisfaction and Boolean Satisfiability. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2015, ISBN 978-3-319-21809-0, pp. 1-103 - Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos:
Feature Selection for High-Dimensional Data. Artificial Intelligence: Foundations, Theory, and Algorithms, Springer 2015, ISBN 978-3-319-21857-1, pp. 1-132
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-12-03 20:35 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: