Valentin Charvet retweetledi
Missed the Machine Learning in Science conference? Catch the recordings now on our YouTube channel!
youtube.com/playlist?list=…
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Valentin Charvet
146 posts
Valentin Charvet
@ValentinCharV
Machine Learning Scientist Reinforcement Learning - Probabilistic Modeling
Katılım Ekim 2019
375 Takip Edilen82 Takipçiler
Happy to announce I passed my viva for my thesis "Dimensionless Bayesian Model-Based Reinforcement Learning"
I thank the examiners Dr Christos Anagnostopoulos and Prof Carl Edward Rasmussen for their comments and insightful discussions.
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Proud of you! (By the way this guy's on the job market 😉)
Valentin Charvet@ValentinCharV
Happy to announce I passed my viva for my thesis "Dimensionless Bayesian Model-Based Reinforcement Learning" I thank the examiners Dr Christos Anagnostopoulos and Prof Carl Edward Rasmussen for their comments and insightful discussions.
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Some teaser before the thesis is published: 2 RL agents trying to solve the cartpole problem with a shifted pole length. Both are trained in the same single environment (with length of 1 meter) but only the one trained in a dimensionless MDP (right fig) transfers succesfully.
GIF
GIF
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And many thanks to my supervisors @MurraySmithRod @ssteinresearch @bjornsandjensen for all your help and patience during these PhD years, I could not have done it without you.
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Valentin Charvet retweetledi
Thank you to our speakers, Dr Paul Kirkland (@PaulKirkland87) and Dr Giulia Marcucci (@GiuliasNLWorld), for their fascinating talks today and thanks to all the members of audience as well!
Stay tuned for more talks to follow!
youtube.com/watch?v=QlYuGP…
YouTube
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Sample efficient model-based RL with Gaussian Processes does not get old:
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Valentin Charvet retweetledi
This is the dream team (plus some online that couldn't make it) that made the first ever @MLinScience conference happen. Huge thanks to speakers and poster presenters for presenting their work and the sponsors that helped fund it!
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Valentin Charvet retweetledi
We hope everyone is enjoying the conference!
Here's a few of the photos we've snapped over the course of the day 📸
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Valentin Charvet retweetledi
Our main talks are split up into three applications of machine learning in science:
• 🏥 Medical Applications
• 🔬 Physics Applications
• 🖥️ Theoretical Machine Learning & Neuroscience
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Very excited to see our first conference set up for tomorrow! We are sold out but you can join online from here uofglasgow.zoom.us/j/88010599533
Machine Learning in Science@MLinScience
The conference starts in just over 14 hours⌛️ We're looking forward to welcoming you all tomorrow for #mlis22 at @UofGARC! An outline of the schedule tomorrow with all of our exciting talks: mlisconf.org/schedule.pdf
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Valentin Charvet retweetledi
👀 Sneak a peek at the deepest & sharpest infrared image of the early universe ever taken — all in a day’s work for the Webb telescope. (Literally, capturing it took less than a day!) This is Webb’s first image released as we begin to #UnfoldTheUniverse: nasa.gov/webbfirstimage…
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Valentin Charvet retweetledi
We finally have the first look at our Milky Way black hole, Sagittarius A*. It’s the dawn of a new era of black hole physics. Credit: EHT Collaboration. #OurBlackHole #SgrABlackHole
Link: eventhorizontelescope.org/blog/astronome…
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When Monte Carlo Tree Search diverges
Brady Neal@CasualBrady
Anyone else experience analysis paralysis?
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Valentin Charvet retweetledi
Today in @nature, with @EPFL, the first deep reinforcement learning system that can keep nuclear fusion plasma stable inside its tokamaks, opening new avenues to advance nuclear fusion research.
Paper: dpmd.ai/fusion-paper
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Valentin Charvet retweetledi
An important intuition about Stochastic Gradient Descent (SGD) is that when you are far from the solutions, individual stochastic gradients will more or less point in the same direction, but at optimum, since their average is 0, they will all point in different directions !
GIF
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Valentin Charvet retweetledi
Great to see the work out at last @gw_hunter ! Well done to Francesco Tonoloni, @CMessengerGW, & Ik Siong Heng! A great collaboration between CS & Physics! @IDAglasgow @GlasgowCS @UofGPhysAstro @QuantIC_QTHub
Dr. Hunter Gabbard@gw_hunter
I'm beyond excited to announce work which was a large part of my thesis has now been published in @NaturePhysics! We show for the first time that a form of #ML can provide estimates on #GW parameters in a fraction of the time of traditional methods. nature.com/articles/s4156…
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