@sigmabayesian
Visiting Researcher @NYU Courant, CILVR. PhD student @TU Denmark, MLLS(https://mlls.dk). Probabilistic ML/DL. Nth order Markovian. Support Manifolds and latents. Previously intern @SonyAI in deep generative modelling. web: http://uppalanshuk.github.io
Excited about
🎓 Generative Modeling Summer School / Statlearn
🗓️ Mar 31 - Apr 4, 2025, France
ℹ️ gemss.ai
🎟️ Deadline: Jan 27, 2025
🧑🏫 Yingzhen Li, @stephanmandt.bsky.social, Aude Sportisse, Anna Korba, @glouppe.bsky.social, @jesfrellsen.bsky.social, @pamattei.bsky.social, @jmtomczak.bsky.social, TBA
🔥You enjoyed @arnauddoucet.bsky.social talk but want even more Schrodinger Bridge? Come talk to me at our poster!
🔷Schrodinger Bridge Flow for Unpaired Data Translation
🔊 East Exhibit Hall A-C #2504
Work done with my amazing collaborators Ira Korshunova
Andriy Mnih and @arnauddoucet.bsky.social
Anne Gagneux, Ségolène Martin, @quentinbertrand.bsky.social Remi Emonet and I wrote a tutorial blog post on flow matching: dl.heeere.com/conditional-... with lots of illustrations and intuition!
We got this idea after their cool work on improving Plug and Play with FM: arxiv.org/abs/2410.02423
This. Training a high dimensional discriminator as a proxy to the log likelihood and doing it so successfully had a ripple effect. Also people forget styleGAN, progressiveGAN and text to image GAN models.
This year, there are 16 positions at CNRS in computer science (8 in "applied" domains → ask me - 8 on "fundamental" domains → ask the other David).
@mathurinmassias.bsky.social has a good list of advice mathurinm.github.io/cnrs_inria_a...
Official 🔗 www.ins2i.cnrs.fr/en/cnrsinfo/...
Don't wait!
Highly interested! Dropping you an email.
Hi Nathan, I am interested in applying but the job link doesn't seem to be working?
For those who don’t know yet, I am organising an online talk series together with Arno Solin on “Advances in Probabilistic Machine Learning (APML)”.
It’s free for everyone to join and support early career researchers!
You can register and check out the schedule here: aaltoml.github.io/apml/
✋
For theoreticians there's just more incentive to find that wall, so they can go back to doing what they were before.
Me as well 🙋. Thanks
Alright, let’s try out that 🦋 thing! I’ll try to post some little pieces about machine learning, uncertainty, and applied maths in general!
