Lenka Zdeborova @zdeborova
Professor at EPFL. Une mathémaphysinformaticienne. Passionate mushroom hunter. Tamer of two little dragons. Switzerland Joined October 2012-
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Presidential Lecture 5/1: Lenka Zdeborová (@zdeborova) on "Statistical Physics of Machine Learning." NYC, 6pm. Free to attend but registration is required: simonsfoundation.org/event/statisti…
Starting tomorrow: If you are around MIT join for the Simons Lectures Series 2024 math.mit.edu
Dynamical phase transitions in graph cellular automata, Freya Behrens, Barbora Hudcová, and Lenka Zdeborová #Networks #ComplexSystems #PhaseTransitions @zdeborova @EPFL_en go.aps.org/4b1DoHu
Talking to many junior faculty members and students in AI lately. Many seem to be somewhat lost with all the seemingly fast progresses made by the industry. My suggestion to them is: It is industry's job to find how to do better, but academia is to find out how to do it right.
Doesn’t everyone feel like that? Those who do not either don’t care about AI or fake it.
Doesn’t everyone feel like that? Those who do not either don’t care about AI or fake it.
Dear Twitter, there was recently a paper showing that different diffusion models generate very similar images when starting with the same initial noise. I did not note the reference. Somebody, please, has it?
Hello Twitter, were the accepted ICML 2024 workshops announced already?
Almost everyone confuses knowledge with intelligence. There’s fundamental difference between absorbing existing knowledge and discovering new one, like the difference between a student and a scientist. That is the reason why existing AI systems require good data to begin with.
My favorite quote by Talagrand reflects well discussions between physicists & probabilists: "When asked why this is the case, @marc_mezard answered: But what else could happen? The more I think about this argument, the more I find it convincing. Yet, it is not fully mathematical"
My favorite quote by Talagrand reflects well discussions between physicists & probabilists: "When asked why this is the case, @marc_mezard answered: But what else could happen? The more I think about this argument, the more I find it convincing. Yet, it is not fully mathematical"
Zdeborová will also participate in a @CornellCIS AI Lunchtime Seminar, “On Generalization and Uncertainty in Learning with Neural Networks,” Friday, March 15 at noon in 122 Gates Hall, with Zoom available in 700 Clark Hall.
This week, I am visiting Cornell. Honoured to give the Bethe lecture series physics.cornell.edu/bethe-lectures. This gave me an occasion to revisit the influence one of Bethe's papers had on how I view the interaction between physics and computer science. A sneak-peak below.
Consider a weak low-rank signal added to a random matrix, the components of the sum are observed through a non-linear function. The exact trade-off between the signal strengths and the non-linearity allowing signal recovery is a topic of our new paper arxiv.org/abs/2403.04234
Clément Canonne @ccanonne_
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Sam Power @sp_monte_carlo
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Krzakala Florent @KrzakalaF
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Helbert Paat @hellohelbert
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Robert @bertran_yorro
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Programmatic 101 @101Programmatic
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Clément Canonne @ccanonne_
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Dan Roy @roydanroy
45K Followers 2K Following ML / AI researcher, emphasis on theory. Research Director and Canada CIFAR AI Chair, @VectorInst Professor, @UofT (Statistics/CS)
Michael Bronstein @mmbronstein
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Kyle Cranmer @KyleCranmer
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Ben Recht @beenwrekt
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Behnam Neyshabur @bneyshabur
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Amin Karbasi @aminkarbasi
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Maxim Raginsky @mraginsky
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NeurIPS Conference @NeurIPSConf
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Max Welling @wellingmax
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Boaz Barak @boazbaraktcs
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Giuseppe Carleo @gppcarleo
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Jason Lee @jasondeanlee
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Ulugbek S. Kamilov @ukmlv
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Peter Richtarik @peter_richtarik
6K Followers 593 Following Federated Learning Guru. Tweeting since 20.5.2020. Lived in 🇸🇰🇺🇸🇧🇪🇬🇧🇸🇦
Krzakala Florent @KrzakalaF
3K Followers 380 Following I like Physics, Statistics, Machine learning, Computer Science & above all playing 🎸. Happy dad 👧 👧. Also professor @ EPFL. Views are my own.
Taco Cohen @TacoCohen
21K Followers 3K Following Deep learner at FAIR. Into codegen, equivariance, generative models. Spent time at Qualcomm, Scyfer (acquired), UvA, Deepmind, OpenAI.
Machine Learning: Sci.. @MLSTjournal
9K Followers 10K Following A multidisciplinary, #openaccess journal devoted to the application and development of #machinelearning for the sciences. Published by @IOPPublishing.
EPFL_AI_Center @EPFL_AI_Center
274 Followers 56 Following A new hub for artificial intelligence, shaping a future where AI works for everyone through cutting-edge research, education, and industrial collaborations.
Fascinating @fasc1nate
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Alessandro Ingrosso @ai_ngrosso
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Blake Bordelon ☕️.. @blake__bordelon
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Steven Strogatz @stevenstrogatz
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Christopher Manning @chrmanning
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Le guide MICHELIN @guideMichelinFR
141K Followers 166 Following Le guide MICHELIN off ! Suivez le quotidien des inspecteurs du guide.
Charlotte Bunne @_bunnech
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Karel Břinda @KarelBrinda
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Petr Šulc @petr_sulc
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Vladka B. Petrakova @PetrakovaVladka
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Matouš Glanc @MGlanc
572 Followers 462 Following Husband | Father | Director @czexpats | Cell biologist | @PSB_VIB @ISTAustria @UniKarlova @MSCActions @EMBOFellows alumnus | Opinions my own
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157 Followers 142 Following Group leader in Ludwig Cancer Research, University of Oxford. Wellcome Trust ECA. Mutagenesis, epigenomics, DNA polymerases, and interdisciplinarity enthusiast.
Jakub Tomek @JakubTomek13
601 Followers 534 Following Computational and experimental cardiology. Tea lover (mainly sheng puerh). Sir Henry Wellcome Fellow at Oxford and UC Davis. Blog https://t.co/7vNqEgWp1d
Ester @furt_v__lihu
4K Followers 189 Following Rozhodně ne Elon Musk, raging feminist, sultán ve svém paláci švandy
David M. Sabatini @DMSabatini
18K Followers 550 Following [email protected] studying rapamycin and mTOR for a long time
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22K Followers 7K Following SumerSports - NFL analytics and roster optimization. Ex-San Diego Padres, Detroit Tigers, Houston Rockets. Google Foobar winner. He/him. [email protected]
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671 Followers 656 Following Promeut la diversité & l’égalité des chances sur les campus @EPFL Fosters diversity & equal opportunity on @EPFL_en campuses #ResponsibleTransformationEPFL 💜
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202 Followers 144 Following Lead ML research @clipdropapp by @StabilityAI & Ph.D in theoretical physics & @MetaAI & Ecole @Polytechnique
We don’t address racism by pretending white people can be black, nor is ableism solved by giving able-bodied people wheelchairs, so why are women and girls supposed to applaud their male imitators, centre them in language and hand over rights their foremothers fought for? 3/X
The latest chapter in the saga of using stochastic localization/denoising diffusions to sample from highly multi-modal Gibbs measures. With the amazing Brice Huang and Huy Tuan Pham. (1/4) arxiv.org/abs/2404.15651
Presidential Lecture 5/1: Lenka Zdeborová (@zdeborova) on "Statistical Physics of Machine Learning." NYC, 6pm. Free to attend but registration is required: simonsfoundation.org/event/statisti…
Starting tomorrow: If you are around MIT join for the Simons Lectures Series 2024 math.mit.edu
@avt_im I’m guessing you’ve Not been on a hiring committee.
Very much looking forward to this. The field moves so fast it can be (very) hard to keep up. These great speakers should give us a lively view. Special thanks to @framigna and @Kamesh_Kris who led the organizing effort.
Our next symposium will focus on large language models. Longer talks and plenty of discussion time should help making these exciting developments more accessible! Please join us. No streaming but recorded talks will be posted @ITSCUNY1 .
On my 🛤️way to 🇨🇭 @EPFL_en 🇨🇭in Neuchatel and Lausanne, for two days of discussions,🫕 and 🍫. I will give a seminar tomorrow on our work at @comedialkb on optical imaging (also on zoom, see QR code below). Looking forward!
Dynamical phase transitions in graph cellular automata, Freya Behrens, Barbora Hudcová, and Lenka Zdeborová #Networks #ComplexSystems #PhaseTransitions @zdeborova @EPFL_en go.aps.org/4b1DoHu
So more rigorously speaking, the pretrained deep large models are better called “artificial knowledge” or “artificial memory”.
Once knowing enough the history of science and engineering research, one shouldn't be surprised that how typical almost an entire community of a field can get trapped in a fundamentally limited approach or framework, and yet not self-aware and often self-celebratory.
Talking to many junior faculty members and students in AI lately. Many seem to be somewhat lost with all the seemingly fast progresses made by the industry. My suggestion to them is: It is industry's job to find how to do better, but academia is to find out how to do it right.
I'm happy to share the recording from my Cornell Bethe lecture a few weeks back. It's a broad-public lecture on "Bridging Physics and Computer Science: Understanding Hard Problems." No technical background is needed. Enjoy! cornell.edu/video/lenka-zd…
I gave a talk about the connection between Bayesian inference and stat-mech at @imperialcollege. I mostly followed the review by @zdeborova and @KrzakalaF but talked a little bit about our upcoming work about inferring random walks. Read the notes here: sunwoo-kim.github.io/en/activities/…
🎉@LightOnIO introduces Mambaoutai 1.6B: A game-changer in AI! With the novel WSD learning rate scheduler & positional weighting, it sets new standards for efficiency & accuracy. Dive into the future of #AI_Language_Models with our latest blogpost➡️lighton.ai/fr/blog/blog-4…
Delighted to announce that Scarlet Schwiderski-Grosche will be joining the EPFL AI Center as Executive Director, starting May 1st. Her demonstrated leadership in cultivating collaborations across various sectors, will be invaluable to the Center. Welcome aboard Scarlet!
Dear Twitter, there was recently a paper showing that different diffusion models generate very similar images when starting with the same initial noise. I did not note the reference. Somebody, please, has it?
@zdeborova I guess the initial work that links the encoder map to the optimal transport map is this one: arxiv.org/pdf/2202.07477… which, in turn, explains the observed similarity between different models
@zdeborova Also check Appendix D.5 in the original arxiv.org/pdf/2011.13456…
@zdeborova I remembered reading it in one of the earlier papers, and I finally found it: it's mentioned in arxiv.org/abs/2011.13456 when introducing the probability flow ODE (bottom of page 7 and appendix D.5)
@zdeborova You might be thinking about this one? arxiv.org/abs/2310.05264 (though this phenomenon was known before this paper) See also arxiv.org/abs/2310.02557, where we show that it happens even for diffusion models trained on different samples from the same training distribution
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