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

Applying ML & deep learning to create AI Assistants & conversational interfaces to create deeper, more personalised one-to-one customer experiences.

Institute for Pure and Applied Mathematics (IPAM), UCLA

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.

Biométrie,    Soft computing, algorithmes évolutionnaires, domaine des métaheuristiques, logique floue

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.

Mathematical Sciences Research Institute (MSRI)

The goal of the workshop is to explore the many emerging connections between the theory of Optimal Transport and models and algorithms currently used in the Machine Learning community. In particular, the use of Wasserstein metrics and the relation between discrete models and their continuous counterparts will be presented and discussed.

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

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.