A joint initiative of CIFAR and Mila, the AI Insights for Policymakers Program connects decision-makers with leading AI researchers through office hours and policy feasibility testing. The next session will be held on October 9 and 10.
Hugo Larochelle appointed Scientific Director of Mila
An adjunct professor at the Université de Montréal and former head of Google's AI lab in Montréal, Hugo Larochelle is a pioneer in deep learning and one of Canada’s most respected researchers.
Mila is hosting its first quantum computing hackathon on November 21, a unique day to explore quantum and AI prototyping, collaborate on Quandela and IBM platforms, and learn, share, and network in a stimulating environment at the heart of Quebec’s AI and quantum ecosystem.
This new initiative aims to strengthen connections between Mila’s research community, its partners, and AI experts across Quebec and Canada through in-person meetings and events focused on AI adoption in industry.
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Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challeng… (see more)e in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples.
Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challeng… (see more)e in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA. We demonstrate the advantages of this framework compared with traditional radio interferometry imaging algorithms, showing that it produces plausible posterior samples despite the use of a misspecified galaxy prior. Through coverage testing on simulations, we empirically evaluate the accuracy of this approach to generate calibrated posterior samples.