Konferenzen zum Thema Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen in Kanada

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1
SpicyFL 2020 — NeurIPS-20 Workshop on Scalability, Privacy, and Security in Federated Learning
05. Dez 2020 - 12. Dez 2020 • Virtual, Kanada
Zusammenfassung:
In the recent decade, we have witnessed rapid progress in machine learning in general and deep learning in particular, mostly driven by tremendous data. As these intelligent algorithms, systems, and applications are deployed in real-world scenarios, we are now facing new challenges, such as scalability, security, privacy, trust, cost, regulation, and environmental and societal impacts. In the meantime, data privacy and ownership has become more and more critical in many domains, such as finance, health, government, and social networks. Federated learning (FL) has emerged to address data privacy issues. To make FL practically scalable, useful, efficient, and effective on security and privacy mechanisms and policies, it calls for joint efforts from the community, academia, and industry. More challenges, interplays, and tradeoffs in scalability, privacy, and security need to be investigated in a more holistic and comprehensive manner by the community. We are expecting broader, deeper, and greater evolution of these concepts and technologies, and confluence towards holistic trustworthy AI ecosystems. This workshop provides an open forum for researchers, practitioners, and system builders to exchange ideas, discuss, and shape roadmaps towards scalable and privacy-preserving federated learning in particular, and scalable and trustworthy AI ecosystems in general.
Eintrags-ID:
1375762
2
NeurIPS 2020 — 34th Annual Conference on Neural Information Processing Systems
06. Dez 2020 - 12. Dez 2020 • Vancouver, Kanada
Zusammenfassung:
We invite submissions for the Thirty-Fourth Annual Conference on Neural Information Processing Systems (NeurIPS 2020), a multi-track, interdisciplinary conference that brings together researchers in machine learning, computational neuroscience, and their applications.
Eintrags-ID:
1359681
3
Continuum Models and Optimisation for Deep Neural Networks
10. Jan 2021 - 15. Jan 2021 • Banff Centre in Alberta, Kanada
Veranstalter:
Banff International Research Station for Mathematical Innovation and Discovery (BIRS)
Zusammenfassung:
Banff's new workshop on Continuum models and optimisation for deep neural networks brings together scientists to discuss the foundations of our digital revolution, and to investigate the reliability of their results. As often, mathematics is the fundamental tool to examine today's challenges. Mathematics structures knowledge and identifies the strengths and limitations of deep learning algorithms. A better understanding of the maths behind deep learning helps us to develop more efficient algorithms and to put safety critical industrial areas such as autonomous driving on a firm ground.
Eintrags-ID:
1364089
4
AAAI-21 — Thirty-Fifth AAAI Conference on Artificial Intelligence
02. Feb 2021 - 09. Feb 2021 • Vancouver, Kanada
Veranstalter:
Association for the Advancement of Artificial Intelligence (AAAI)
Zusammenfassung:
The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines.
Eintrags-ID:
1359290
5
Advances in Stein’s Method and its Applications in Machine Learning and Optimization
11. Apr 2021 - 16. Apr 2021 • Banff Centre in Alberta, Kanada
Veranstalter:
Banff International Research Station for Mathematical Innovation and Discovery (BIRS)
Zusammenfassung:
Recently a variety of state-of-the-art methods in machine learning and artificial intelligence have been developed motivated by techniques from Stein’s method, a successful tool from the field of probability theory. These methods have enabled efficient analysis of the large amounts of data being produced in several scientific fields, like neuroscience, information technology, and finance. Motivated by this success, there has been an ever increasing interest in exploring further connections between Stein’s method and machine learning. The focus of this workshop is to consolidate isolated efforts and develop a theoretically principled inferential and computational framework via Stein's method for analyzing increasingly complex models and data objects. This workshop is intended to bring together prominent and promising young and diverse researchers working on Stein’s method and machine learning, and to charter the path for future development in the field.
Eintrags-ID:
1364163
6
Mathematical Statistics and Learning
28. Nov 2021 - 03. Dez 2021 • Banff Centre in Alberta, Kanada
Veranstalter:
Banff International Research Station for Mathematical Innovation and Discovery (BIRS)
Zusammenfassung:
The workshop focuses on the novel mathematical challenges of statistics and machine learning. The spectacular success of machine learning in a wide range of applications opens many exciting theoretical challenges in a number of mathematical fields, including probability, statistics, combinatorics, optimization, and geometry. BIRS will bring together researchers of machine learning and mathematical statistics to discuss these problems. The principal topics include combinatorial statistics, online learning, and deep neural networks.
Eintrags-ID:
1364451


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Stand vom 20. Oktober 2020