Konferenzen  >  Mathematik  >  Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen  >  Vereinigte Staaten

Institute for Computational and Experimental Research in Mathematics, Brown University, Providence, RI (ICERM)

The aim of this workshop is to bring together participants from computational mathematics and gravitational wave astronomy to tackle computational challenges in leveraging data-driven methods in key areas of gravitational wave data analysis in order to maximize the science output of the ongoing and upcoming observations. The areas of focus will be: (i) noise classification and detection, (ii) waveform modeling and uncertainty quantification, and (iii) source parameter and astrophysical population Bayesian inference.

TechWell

Welcome to the cutting-edge world of artificial intelligence and machine learning. AI Con USA brings together leading experts, researchers, innovators, and companies from around the globe to explore the latest developments in AI and machine learning. Whether you are a seasoned professional, a researcher, or an enthusiast eager to stay at the forefront of AI and machine learning, AI Con USA provides a dynamic environment for knowledge exchange, collaboration, and inspiration.

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AI, artificial intelligence, machine learning, LLM, RAG, software engineering

Fora Group

Ai4 2025, held at the MGM Grand in Las Vegas, is North America’s largest AI industry conference, gathering over 8,000 attendees, 600+ speakers, & 250+ exhibits. Discover the latest applications in AI, explore best practices shaping the future of AI, and enjoy unbeatable networking. Join us as at the epicenter of the global AI community.

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Artificial Intelligence

Institute for Computational and Experimental Research in Mathematics, Brown University, Providence, RI (ICERM)

Can machines prove theorems? Can they have mathematical ideas? On one hand, category theory offers a formalism for axiomatising ideas from machine learning. On the other hand, mathematicians are excited about the prospect of utilising machine learning techniques to spot new patterns in vast swathes of combinatorial data and hence formulate new conjectures. The purpose of this workshop is to bring together experts from across algebraic combinatorics, category theory, and machine learning in order to make headway on topics at the intersection of these fields.