Conférences - Réseaux de neurones et intelligence artificielle, apprentissage automatique - États-Unis

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

Mathématiques appliquées (en général),    Robotique, automation & mécatronique

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

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

Data Science, Artificial Intelligence, Machine Learning

The purpose of the Neural Information Processing Systems annual meeting is to foster the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. The core focus is peer-reviewed novel research which is presented and discussed in the general session, along with invited talks by leaders in their field. On Sunday is an Expo, where our top industry sponsors give talks, panels, demos, and workshops on topics that are of academic interest. On Monday are tutorials, which cover a broad background on current lines of inquiry, affinity group meetings, and the opening talk & reception. The general sessions are held Tuesday - Thursday, and include talks, posters, and demonstrations. Friday - Saturday are the workshops, which are smaller meetings focused on current topics, and provide an informal, cutting edge venue for discussion.

The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21) is a venue for papers describing highly innovative realizations of AI technology. The objective of the conference is to showcase successful applications and novel uses of AI. The conference will use technical papers, best practice papers, invited talks, and panel discussions to explore issues, methods, and lessons learned in the development and deployment of AI applications; and to promote an interchange of ideas between basic and applied AI and the discourse on the actual deployment of AI in practice.

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?

Géophysique et géologie,    Métrologie et instrumentation

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.