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The International Centre for Mathematical Sciences (ICMS)

In the last few years, an important trend has emerged for using data-driven image models, in particular encoded by neural networks. Novel families of hybrid imaging methodologies, mixing data-driven and traditional mathematical approaches (such as optimisation or sampling methods) have flourished. For instance, generative or discriminative networks such as GANS, VAEs or normalising flows, can be either used in optimisation or sampling schemes as data-driven regularisers for solving inverse problems. Similarly, denoising networks or more generally regularising networks can be incorporated into optimisation or sampling schemes leading to Plug-and-Play methods. From another perspective, unrolled optimisation approaches have been investigated to provide robust network architectures as alternative to traditional black-box end-to-end networks. All these approaches have shown a remarkable versatility and efficiency to solve inverse imaging problems.

Numerische Analysis,    Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen

TechIConf

The NextGen Intelligence 2026 Conference, taking place on November 16–17, convenes industry leaders, academic experts, technology pioneers, data scientists, cloud strategists, and AI innovators to collaborate, exchange insights, and address emerging global and regional challenges across Data, Cloud, and Artificial Intelligence. Hosted in London, NextGen Intelligence 2026 will deliver strategic insights and practical expertise through focused sessions, interactive forums, technology showcases, regulatory perspectives, and real-world case studies - bringing together diverse global viewpoints to accelerate innovation, digital transformation, and intelligent enterprise growth.

Event Secretariat;     Tel.: [+13023966954];     Email: info@techaiconf.com

AI Innovations, Data Science, Cloud Transformation, Data Engineering, Cloud Systems, Generative AI, Big Data, Data lakes

Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen,    Edge/Cloud-Computing