Alexandre Alahi

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Alexandre Alahi

Alexandre Alahi

@AlexAlahi

Professor at EPFL (École Polytechnique Fédérale de Lausanne) @EPFL_en / Director of the VITA lab, @StanfordAILab

Lausanne, Suisse Katılım Aralık 2010
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Alexandre Alahi
Alexandre Alahi@AlexAlahi·
🎉 Exciting news!  Our team has achieved top rank on the nuScenes trajectory prediction leaderboard! Our strategy? Unprecedented data scaling with our new open-source framework . Project page: tinyurl.com/2sfendzc Special thanks to Lan, hossein, kaouther, Eloi, & Matthieu!
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Kathrin Grosse
Kathrin Grosse@KathrinGrosse·
Dear All, are you working in industry with AI/ML or know someone who does? You are dearly needed! It is time for another survey to threat model ML/AI in practice. Please take 10 minutes to click through our questions or share with someone who can help: redcap.epfl.ch/surveys/?s=PXD…
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Yuejiang Liu
Yuejiang Liu@liu_yuejiang·
Distribution shifts are grand challenges for machine learning models. When it comes to motion, what are the unique problems and opportunities? We provide an answer in our #CVPR2022 paper: “Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective” 1/6
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Alexandre Alahi
Alexandre Alahi@AlexAlahi·
Thank you Andrej @karpathy (Director of AI at Tesla) for the great interactive Q&A during our “CIVIL 459: Deep Learning for Autonomous Vehicles" class. We all loved the interactions (including our robots!).
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Pierre Vandergheynst
Pierre Vandergheynst@trekkinglemon·
At @EPFL_EN we believe Machine Learning is poised to fundamentally change the way we do Science and engineer solutions to our most pressing challenges. If you’re a young researcher sharing this passion, apply to our new #AI4Science fellowship: epfl.ch/research/domai…
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Yuejiang Liu
Yuejiang Liu@liu_yuejiang·
Key takeaway * both take-specific info (e.g. SSL) and model-specific info (e.g. feature moments) are crucial * we should rethink what to store, in addition to model parameters, for robust deployment Link: bit.ly/3GgsqhF w/ Parth, Bastien, Baptiste, Taylor, @AlexAlahi
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Yuejiang Liu
Yuejiang Liu@liu_yuejiang·
Test-time training (TTT) is an emerging approach to tackle distributional shifts. Yet, it's still not robust for practical use. Have you ever wondered when TTT fails or thrives? Our paper TTT++ #NeurIPS2021 provides an answer. To know more, plz join us at Session 1 this Tue :)
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