Konferenzen zum Thema Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen in den Vereinigten Staaten (USA)

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1
Machine Learning for Physics and the Physics of Learning
04. Sep 2019 - 08. Dez 2019 • Institute for Pure and Applied Mathematics (IPAM),, Vereinigte Staaten
Zusammenfassung:
Machine Learning (ML) is quickly providing new powerful tools for physicists and other natural scientists to extract essential information from large amounts of data, either from experiments or simulations. This IPAM long program will foster nontrivial research and provoke scientific discussion at the interface between ML and Physics. We aim to go beyond simple fitting of physical models from data and move the discussion to (i) using generative ML methods and active learning in order to generate and design complex and novel physical structures and objects, (ii) obtain models that are physically understable, e.g. by maintaining relations of the predictions to the microscopic physical quantities used as an input, (iii) using ML to learn the physical principles and mathematical structures underlying the data, and (iv) developing new ML methods inspired by methods and models developed in Physics.
Eintrags-ID:
1065531
2
ADT 2019 — 6th International Conference on Algorithmic Decision Theory
25. Okt 2019 - 27. Okt 2027 • Durham, NC, Vereinigte Staaten
Zusammenfassung:
The ADT 2019 conference focus is on algorithmic decision theory broadly defined, seeking to bring together researchers and practitioners coming from diverse areas of Computer Science, Economics and Operations Research in order to improve the theory and practice of modern decision support.
Themen:
Algorithms, Argumentation Theory, Artificial Intelligence, Computational Social Choice, Database Systems, Decision Analysis, Discrete Mathematics, Game Theory, Machine Learning, Matching, Multi-agent Systems, Multiple Criteria Decision Aiding, Networks, Optimization, Risk Management, and Utility Theory
Eintrags-ID:
1193948
3
Workshop IV: Using Physical Insights for Machine Learning
18. Nov 2019 - 22. Nov 2019 • Los Angeles, CA, Vereinigte Staaten
Veranstalter:
Institute for Pure and Applied Mathematics (IPAM), UCLA
Zusammenfassung:
Part of the Long Program Machine Learning for Physics and the Physics of Learning.

In this workshop we will explore how to use physical intuition and ideas to design new classes of machine learning (ML) algorithms. Physics-inspired sampling algorithms could be used to train ML structures or sample the hyper-parameter space (e.g. deep Neural Networks). Additionally, physics-based models such as Ising/Potts models or energy-based models have influenced ML inference frameworks such as Markov Random Fields and Restricted Boltzmann Machines, and we want to continue the discussion to facilitate this innovation transfer. Finally, physical insight could be used to enhance learning in the situation of scarce data by enforcing smoothness, differentiability or other physical properties relevant to a given problem.

Eintrags-ID:
1170895
4
AI3D 2019 — 2nd International Workshop on Artificial Intelligence for 3D Big Spatial Data Processing
09. Dez 2019 - 11. Dez 2019 • San Diego, Kalifornien, Vereinigte Staaten
5
ICMLA 2019 — 18th International Conference on Machine Learning and Applications
16. Dez 2019 - 19. Dez 2019 • Boca Raton, FL, Vereinigte Staaten
Zusammenfassung:
The conference provides a leading international forum for the dissemination of original research in ML, with emphasis on applications as well as novel algorithms and systems. Following the success of previous ICMLA conferences, the conference aims to attract researchers and application developers from a wide range of ML related areas, and the recent emergence of Big Data processing brings an urgent need for machine learning to address these new challenges. The conference will cover both machine learning theoretical research and its applications. Contributions describing machine learning techniques applied to real-world problems and interdisciplinary research involving machine learning, in fields like medicine, biology, industry, manufacturing, security, education, virtual environments, games, are especially encouraged.
Eintrags-ID:
1226528
6
100th AMS Annual Meeting — 19th Conference on Artificial Intelligence for Environmental Science
12. Jan 2020 - 16. Jan 2020 • Boston, MA, Vereinigte Staaten
7
AI NEXTCon
23. Jan 2020 - 26. Jan 2020 • Seattle, WA, Vereinigte Staaten
Eintrags-ID:
1246674
8
Workshop — Deep Learning and Medical Applications
27. Jan 2020 - 31. Jan 2020 • Los Angeles, CA, Vereinigte Staaten
Veranstalter:
Institute for Pure and Applied Mathematics (IPAM), UCLA
Zusammenfassung:
Rapid advances in deep learning techniques are starting to revolutionize medical imaging. Radiology, disease detection, and tissue imaging are all expected to be facilitated by automated image analysis programs in the near future. Many new interdisciplinary research questions arise; finding solutions with practical significance requires input from mathematicians, bio-physicists, and computational engineers. This workshop aims to bring together researchers from different backgrounds to explore this new frontier of science.
Eintrags-ID:
1172922
9
Rework Applied AI Summit
30. Jan 2020 - 31. Jan 2020 • San Francisco, CA, Vereinigte Staaten
10
Rework Deep Learning Summit
30. Jan 2020 - 31. Jan 2020 • San Francisco, CA, Vereinigte Staaten
11
Rework AI Assistant Summit
30. Jan 2020 - 31. Jan 2020 • San Francisco, CA, Vereinigte Staaten
Zusammenfassung:
Applying ML & deep learning to create AI Assistants & conversational interfaces to create deeper, more personalised one-to-one customer experiences.
Eintrags-ID:
1246759
12
AAAI 2020 — AAAI Conference on Artificial Intelligence
07. Feb 2020 - 12. Feb 2020 • New York, Vereinigte Staaten
Eintrags-ID:
1242390
13
ALT — Algorithmic Learning Theory
08. Feb 2020 - 11. Feb 2020 • San Diego, Vereinigte Staaten
Eintrags-ID:
1250347
14
Computational Psychiatry
18. Feb 2020 - 21. Feb 2020 • Los Angeles, CA, Vereinigte Staaten
Veranstalter:
Institute for Pure and Applied Mathematics (IPAM), UCLA
Zusammenfassung:
Psychiatric disorders are typically diagnosed and evaluated using subjective psychological exams that assess symptoms, thoughts, feelings and behavioral patterns. Ongoing and recent advances in measurements provide EEG, functional MRI, optogenetic, genomic, and metabolic data. Along with mathematical methods developed to analyze these data, a more physiological and quantitative approach for diagnosis and treatment can be envisioned. This workshop will explore how modern computational tools and mathematical modeling can be integrated with measurements to improve psychiatric diagnosis and treatment.
Eintrags-ID:
1172997
15
O'Reilly AI Conference
27. Apr 2020 - 30. Apr 2020 • New York, NY, Vereinigte Staaten
Zusammenfassung:
Organizations that successfully apply AI tools and strategies compete more effectively. Make plans to join us at the O'Reilly AI Conference and chart your business transformation.
Eintrags-ID:
1246764
16
AVWS1 — Workshop: Individual Vehicle Autonomy: Perception and Control
05. Okt 2020 - 09. Okt 2020 • Los Angeles, California, Vereinigte Staaten
Veranstalter:
IPAM
Zusammenfassung:
This workshop will bring together researchers working on the theoretical sides of deep learning techniques for perception and control of automated vehicles with researchers interested in assuring these autonomous systems operate with safety guarantees. Moreover, experts in sensing and imaging technology will be brought to the table, to cover the full pipeline from the collection of the data, over the AI theory and development, all the way to the software and actuation challenges. Additional themes addressed in this workshop include interactions between vehicle sensing and the infrastructure, and cybersecurity aspects related to sensing and machine learning (how to purposefully mislead sensors and AI).
Themen:
Part of the Long Program Mathematical Challenges and Opportunities for Autonomous Vehicles
Eintrags-ID:
1275206
17
Searches and Machine Learning Meet the Precision Frontier
12. Apr 2021 - 15. Apr 2021 • UC Santa Barbara, Vereinigte Staaten
Veranstalter:
Kavli Institute for Theoretical Physics (KITP)
Zusammenfassung:
The Large Hadron Collider (LHC) is the world’s facility for probing fundamental physics at the electroweak scale and well beyond. As it enters a new phase of extended data accumulation, two broad challenges emerge: how to fulfill the potential for percent-level precision with the large dataset, and how to maximise the information that can be extracted from each event about the underlying scattering process, in particular with machine learning. Solving these problems will have an impact across a wide range of physics topics: establishing the properties of the newly discovered interactions of the Higgs sector and understanding electroweak symmetry breaking, enhancing the sensitivity of searches for physics beyond the Standard Model (BSM), and precision measurements of a range of fundamental parameters in the Standard Model.
Eintrags-ID:
1246600
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Stand vom 20. Oktober 2019