Gauge Equivariant CNNs, Generative Models, and the Future of AI with Max Welling - This Week in Machine Learning & AI

Gauge Equivariant CNNs, Generative Models, and the Future of AI with Max Welling - This Week in Machine Learning & AI

Sam is joined by Max Welling, VP of technologies at Qualcomm, to discuss his research at Qualcomm AI Research and the University of Amsterdam.

15 mentions: @wellingmax@twimlai@me_datapoint@twimlai@twimlai@twimlai@sk121@twimlai
Date: 2019/05/20 14:59

Referring Tweets

@twimlai We’re joined by @wellingmax, research chair in machine learning at the University of Amsterdam, & VP of technologies at @Qualcomm, & Fellow at the Canadian Institute for Advanced Research, or CIFAR. Hear Max share his research at Qualcomm AI & more at t.co/IwZyfoEGy8 t.co/Yzy8C9Q5RQ
@me_datapoint Interesting interview of @wellingmax by @twimlai: t.co/3FIVepQHGI In last 3rd coming to his recent essay t.co/oEXjbVQO03 and arguing for why model based RL/DL could be key for dealing with rare events (and life is full of those!)!
@twimlai Bayesian deep learning, Graph CNNs and Gauge Equivariant CNNs, and in power efficiency for AI via compression, quantization, and compilation are a few topics @Qualcomm's @wellingmax shared with us in our recent conversation. Catch our conversation at t.co/eCPGZuv4k0 t.co/jvcN3Tz2wc
@twimlai We’re joined by @wellingmax, of @Qualcomm, as he discusses his research on Bayesian deep learning, Graph CNNs and Gauge Equivariant CNNs, and his thoughts on the future of the AI industry, in particular the relative importance of models, data and compute. t.co/eCPGZuv4k0 t.co/hxSi5JGISu
@twimlai ICYMI, @wellingmax discusses how @Qualcomm has been actively involved in AI research for well over a decade, leading to advances in power-efficient on-device AI as well as in algorithms such as Bayesian deep learning, graph CNNs, & more. Listen at t.co/OXD1DjJqqW t.co/dJyYOf95lh
@twimlai Today we’re joined by @WellingMax, VP of Technologies at @Qualcomm, to discuss his research at Qualcomm AI Research and the University of Amsterdam, including his work on Bayesian deep learning, along with his thoughts on the future of the AI industry. t.co/s34i1lJSfC
@sk121 This is a fantastic episode of TWIML&AI podcast inteviewing Max Welling. He eloquently describes how borrowing ideas from fundamental physics around local symmetries can make deep learning more powerful and then discussing the evo…t.co/Y6etXD1tu5 t.co/LSwBknLecd
@twimlai In our conversation, we discuss @wellingmax’s research at @Qualcomm AI Research and the university, including his work on Bayesian deep learning, Graph CNNs and Gauge Equivariant CNNs, & in power efficiency for AI via compression, quantization, and more. t.co/MWpkigFmZX t.co/BKvzmFItLh
@twimlai Tune in for a weekend listen with @wellingmax. Max joins us to discuss "Gauge Equivariant CNNs, Generative Models, and the Future of AI." Hear about his research at @Qualcomm and the University of Amsterdam and more at t.co/OXD1DjJqqW t.co/dqHjoymVxI

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