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lisa_seminaires@iro.umontreal.ca

May 2019

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Re: [Lisa_seminaires] [mila-tous] [Tea Talk] Dan Sodickson (NYU) Fri 17 May 2019 10h30 Auditorium H2
by Joseph Paul Cohen 14 May '19

14 May '19

Can we please use a public calendar? After all the debates about the "right way" to do calendars mine does not show that we have a tea talk on Friday. On Tue, May 14, 2019 at 11:47 AM Rim Assouel <rim.assouel(a)gmail.com> wrote: > > This week we have Dan Sodickson from NYU giving a talk on Machine Learning and Medicine: How AI will change the way we see patients, and the way we see ourselves. at 10h30 in Auditorium H2. > > Auditorium H2 is on the 2nd floor of the 6650 building. Indications will be put from the agora and the elevators as well ! > > Will this talk be streamed ? yes > > See you there! > The Tea Talk Team > > TITLE Machine Learning and Medicine: How AI will change the way we see patients, and the way we see ourselves. > > ABSTRACT > Just as astronomy constitutes the exploration of outer space, advances in medicine may be seen as an ever-deeper exploration of inner space. This talk will explore how that inwardly-focused exploration may be transformed in the age of machine learning. I will begin by attempting to convey a view of artificial intelligence from the vantage point of medicine: what physicians tend to feel about AI, what they tend to know, and what they generally do not know. I will briefly cite examples of productive (and less productive) emerging uses of AI in medicine. I will then focus on medical imaging in particular, and will summarize some of the goals, early outcomes, challenges, and future directions of the fastMRI collaboration between NYU School of Medicine and Facebook AI Research, in which deep learning is being used to accelerate MRI beyond previous limits. In addition to being of great value to patients, physicians, and healthcare systems, acceleration serves as a potent enabler of several emerging trends that promise to reshape the future of biomedical imaging, including a move from carefully-staged snapshots to continuous streaming, and a move from imitating the eye to emulating the brain. In this context, I will explore how AI may change not only the analysis of images and other sensor data streams, but also the design and use of future imaging devices. I will conclude with a few speculations about potential changes in the way we interact with the medical system and even how we perceive the world around us. Throughout, I will attempt to highlight areas in which data scientists can add value to the day-to-day practice of medical imaging, to the improvement of human health, and to the ongoing exploration of inner space. > > BIO > Daniel K. Sodickson MD, PhD is Vice-Chair for Research in the Department of Radiology at NYU Langone Health, Professor of Radiology and Physiology & Neuroscience at NYU School of Medicine, and Professor of Biomedical Engineering at the NYU Tandon School of Engineering. He has led a transformation of imaging research at NYU Langone, bringing the Radiology Department’s national research ranking from #17 to #5, and earning the department’s Center for Advanced Imaging Innovation and Research (CAI2R) a designation as a national Biomedical Technology Resource Center. Dr. Sodickson’s research aims at seeing what has previously been invisible, in order to improve human health. He is credited with founding the field of parallel imaging, in which distributed arrays of detectors are used to gather magnetic resonance images at previously inaccessible speeds. Parallel imaging hardware and software is now an integral part of MRI machines, and is used routinely in MRI scans worldwide. In 2006, Dr. Sodickson was awarded the Gold Medal of the International Society for Magnetic Resonance in Medicine (ISMRM), and he recently completed a term as ISMRM president. Last year, he helped to initiate the fastMRI collaboration between NYU and FAIR, and he continues to work closely with colleagues at FAIR. He is currently in the process of launching a new institute – Tech4Health – designed to bring emerging technologies such as continuous sensing and artificial intelligence to biomedicine. > > -- > You received this message because you are subscribed to the Google Groups "MILA Tous" group. > To unsubscribe from this group and stop receiving emails from it, send an email to mila-tous+unsubscribe(a)mila.quebec.

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[Double Tea Talk] Marc Bellemare (Brain) x Pieter Abbeel (UC Berkeley) Fri 24 May 10h30 Mila Auditorium
by Rim Assouel 06 May '19

06 May '19

On May the 24th we will have a special double tea talk (40min each) featuring Marc G. Bellemare and Pieter Abbeel from 10.30AM to 12PM at Mila Auditorium! We send the notification email early enough so that you can all mark your calendars :) Those talks will be streamed and recorded here <https://mila.bluejeans.com/4862024040/webrtc> ! See you there, The Tea Talk Team :) TITLE Some progress on understanding the benefits of distributional reinforcement learning ABTRACT In this talk I will review what we now know about distributional reinforcement learning and how its full benefits are only obtained when combined with nonlinear representations such as deep networks. I will discuss how trying to understanding the good empirical performance of distributional RL has led us to all kinds of exciting results regarding representation learning for RL, and in particular a formulation of optimal representation learning based on the geometric notion of a value function polytope. BIO Marc G. Bellemare leads the reinforcement learning efforts at Google Brain in Montreal and holds a Canada CIFAR AI Chair at the Quebec Artificial Intelligence Institute (Mila). He received his Ph.D. from the University of Alberta, where he developed the highly-successful Arcade Learning Environment, the platform that sparked the recent revival in deep reinforcement learning research. He joined DeepMind in 2013 prior to its acquisition by Google and was research scientist there until his return to Canada in 2017. During his tenure at DeepMind he made important contributions to deep reinforcement learning, in particular pioneering the distributional method. Marc G. Bellemare is also a CIFAR Learning in Machines & Brains Fellow and an adjunct professor at McGill University. …………. TITLE Model-based RL via Meta-Model-Free RL ABSTRACT Model-free RL has seen great asymptotic successes, but sample complexity tends to be high. Model-based RL carries the promise of better sample efficiency, and indeed has shown more data efficient learning, but tends to fall well short of model-free RL in terms of asymptotic performance. In this presentation I will describe a new approach to model-based RL that brings in ideas from domain randomization and meta-model-free RL, resulting in the best of both worlds: fast learning and great asymptotic performance. Our method is evaluated on several mujoco environments (PR2 reacher, swimmer, hopper, ant, swimmer, walker) and is able to learn lego-block placement on a real robot in 10 minutes. BIO Professor Pieter Abbeel is Director of the Berkeley Robot Learning Lab and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab. Abbeel’s research strives to build ever more intelligent systems, which has his lab push the frontiers of deep reinforcement learning, deep imitation learning, deep unsupervised learning, transfer learning, meta-learning, and learning to learn, as well as study the influence of AI on society. His lab also investigates how AI could advance other science and engineering disciplines. Abbeel has founded three companies: Gradescope, Covariant, and Berkeley Open Arms, advises many AI and robotics start-ups, and is a frequently sought after speaker worldwide for C-suite sessions on AI future and strategy. Abbeel has received many awards and honors, including the PECASE, NSF-CAREER, ONR-YIP, Darpa-YFA, TR35. His work is frequently featured in the press, including the New York Times, Wall Street Journal, BBC, Rolling Stone, Wired, and Tech Review.

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