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

IPAM

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).

Part of the Long Program Mathematical Challenges and Opportunities for Autonomous Vehicles

Angewandte Mathematik (allgem.),    Robotik, Automatisierung & Mechatronik

ODSC

ODSC West 2020 is going virtual! We’re thrilled to be hosting our third virtual conference October 27th-30th. Featuring 100+ hands-on training sessions and workshops, 240 speakers, and 320 hours of content, ODSC West Virtual Conference is the perfect opportunity to gain new in-demand skills and network with your peers and our renowned speakers wherever you are. Get your pass today to save 70% on your ticket to ODSC West Virtual Conference. https://odsc.com/california/#register

Tel.: [+1 510 984 8238];     Email: info@odsc.com

Data Science, Artificial Intelligence, Machine Learning

SEG - Society of Exploration Geophysicists

The most significant contribution of this event is the Emergence of the utilization of data analytics and machine learning for geoscience. This workshop is designed to showcase the successes and challenges of practicing machine learning and data analytics in the geoscience domain to improve accuracy and efficiency of algorithms and/or workflows. For example, in addition to existing applications such as seismic geobody identification and well log data analysis, what other challenging geoscience problems can be formulated and solved effectively by machine learning? How can machine learning algorithms and workflows be tailored to meet the specific physical constraints of geoscience data? How can we fully exploit the power of machine learning and physics-based approaches?

Geophysik und Geologie,    Messtechnik

Kavli Institute for Theoretical Physics (KITP)

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.