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

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.

O’Reilly + Intel AI

The AI Conference attracts the brightest minds in AI: algorithm and perception engineers, research and machine learning scientists, data scientists and data engineers, program and product managers, innovation officers and business leaders who come together to share ideas and to explore the most essential—and intriguing—topics in applied AI.

Join 600+ senior technology leaders from AI chip start-ups, semiconductor companies, system vendors/OEMs, data centers, end users, financial services, investors and fund managers, to build a comprehensive architectural roadmap of the emerging AI chip market.

Computerhardware,    Software-Agenten und künstliche Intelligenz

Institute for Pure and Applied Mathematics (IPAM), UCLA

Part of the Long Program Machine Learning for Physics and the Physics of Learning.

The workshop will examine how to plan experiments in order to use information in a cost-optimal way. It will also include the application of these modalities to training complex models, such as deep architectures, and the transfer of these ideas to the generation of physically-relevant complex structures such as chemical structures, molecular structures, scalar or vector fields in fluid dynamics or electrodynamics, proposal steps for Markov chain Monte Carlo of physical systems etc.

Part of the Long Program Machine Learning for Physics and the Physics of Learning.

The workshop will include methods to summarize and interpret a complicated learned model (e.g. deep neural network) by interrogating this model about what and why it has learned (e.g. relevance propagation and sensitivity analysis).

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.

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

Optimierungsrechnung, Entscheidungsunterstützung,    Informations & Wissensmanagement, Big Data Computing

Institute for Pure and Applied Mathematics (IPAM), UCLA

Part of the Long Program Machine Learning for Physics and the Physics of Learning.

This workshop will showcase how to employ mathematical aspects of statistical / information theoretic approaches in ML for the discovery of physical laws from data. Offering statistical guarantees along with the learned models is critical in physics and in areas such as aeronautics, climate science, chemistry, biology, and robotics. We will consider model selection, robust statistics, model-free and adaptive learning, and model validation in the context of both static and dynamic models, such as equations of motion.

ICTAI 2019: The IEEE International Conference on Tools with Artificial Intelligence (ICTAI) is a leading Conference of AI in the Computer Society providing a major international forum where the creation and exchange of ideas related to artificial intelligence are fostered among academia, industry, and government agencies.

Soft Computing, Evolutionäre Algorithmen, metaheuristische Optimierung, Fuzzylogik ,    Software-Agenten und künstliche Intelligenz

Institute for Pure and Applied Mathematics (IPAM), UCLA

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.

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.

American Meteorological Society (AMS)

Institute for Pure and Applied Mathematics (IPAM), UCLA

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.

Angewandte Mathematik (allgem.),    Mustererkennung und Bildverarbeitung, Maschinelles Sehen

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.

Biometrie,    Soft Computing, Evolutionäre Algorithmen, metaheuristische Optimierung, Fuzzylogik

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.

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.