Today we’re joined by Max Welling, Vice President of Technologies at Qualcomm Netherlands, and Professor at the University of Amsterdam.
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In case you missed it, Max joined us last year to discuss his work on Gauge Equivariant CNNs and Generative Models–the 2nd most popular episode of 2019.
In this conversation, we explore the concept and Max’s work in neural augmentation, and how it’s being deployed for channel tracking and other applications. We also discuss their current work on federated learning and incorporating the technology on devices to give users more control over the privacy of their personal data. Max also shares his thoughts on quantum mechanics and the future of quantum neural networks for chip design.
Thanks to our sponsor
Before dive into today’s show, I’d like to send a huge thank you to our friends at Qualcomm for their support of the podcast and their sponsorship of this episode! Qualcomm AI Research is dedicated to advancing AI to make its core capabilities — perception, reasoning, and action — ubiquitous across devices. Their work makes it possible for billions of users around the world to have AI-enhanced experiences on Qualcomm Technologies-powered devices. To learn more about what Qualcomm is up to on the research front, visit twimlai.com/qualcomm.
Connect with Max!
- Paper: Data-driven reconstruction of gravitationally lensed galaxies using recurrent inference machines
- Paper: Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs
- Paper: Natural Graph Networks
- Paper: Federated Learning of User Authentication Models
- Paper: Recurrent Inference Machines for Solving Inverse Problems
- #267 – Gauge Equivariant CNNs and Generative Models
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“More On That Later” by Lee Rosevere licensed under CC By 4.0