
Vincent Dutordoir
@vdutor
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Research Scientist @GoogleDeepMind (AI for Science). Previously: PhD @CambridgeMLG, @SecondmindLabs
Cambridge, England
Joined April 2017
RT @GoogleDeepMind: Introducing AlphaGenome: an AI model to help scientists better understand our DNA – the instruction manual for life 🧬….
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An opportunity to join our team. Feel free to reach out if you have any questions.
We are looking a PhD Student Researcher at Google DeepMind for 2025 summer! Strong publication record and technical skills on machine learning and genomics preferred. Need to be 80% for a few months. Team in London. Looking forward to hear from you!
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RT @shreyaspadhy: I’m really excited to be attending NeurIPS and presenting our work on efficient fine-tuning of pre-trained diffusion mode….
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A highlight of the #NeurIPS2023 meetup in Cambridge for me was Carl Rasmussen's keynote on "Halting Climate Change". Spoiler: it has nothing to do with AI. Worth a watch:
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Happy to share Geometric Neural Diffusion Processes at #NeurIPS. Join us for Poster Session 3 on Wednesday if you’re interested in diffusion models for modelling equivariant stochastic processes. With @MathieuEmile @MHutchinson141 @ValentinDeBort1 @yeewhye #richturner.
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There are still a couple of places left for the NeurIPS@Cambridge meetup next week, Dec 8th. Priority will be given to people presenting a poster. Registration & full schedule on
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Happy to announce the 3rd NeurIPS @ Cambridge event on Friday, Dec 8th! Join us for an in-person gathering to discuss the latest in machine learning research. Find all details and register at #NeurIPS2023 #MachineLearning #Cambridge
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RT @frgsimpson: Our #ICML2023 paper introduces Neural Diffusion Processes - generalising diffusion processes to function spaces. Great to w….
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RT @louistiao: in case you ever wondered what happens when a sphere gets hit with the ReLU activation🌐🧠🔥
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Congrats! It's amazing to see how Random (Fourier) Features continue to fuel progress even in this era of Transformers.
Simplex Random Features accepted to #ICML2023 w oral presentation! TLDR: Monte Carlo methods linearise Transformer attention by throwing a (random) kitchen sink at it, but you can get much better results with a slightly more sophisticated sink🚰 🧵1/9
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