Who's Who of AI

David Picard

178 trust researcher @davidpicard.eurosky.social · 2,888 followers
Professor of Computer Vision/Machine Learning at Imagine/LIGM, École nationale des Ponts et Chaussées @ecoledesponts.bsky.social Music & overall happiness 🌳🪻 Born well below 350ppm 😬 mostly silly personal views 📍Paris 🔗 https://davidpicard.github.io/
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Articles & links

Babe, stop everything! New favorite paper of the year is out! kyutai.org/fid-lottery/ arxiv.org/abs/2606.20536

The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation arxiv.org
AI Weekly's analysis →
  • Retraining a model with a different seed moves its FID score 3.2x more than resampling from a fixed trained network.
  • FID coefficient of variation stays within a 1-2% band even as compute or model size increases.
  • The authors recommend treating any FID gap below roughly 1.3% CoV as inconclusive and requiring multi-seed error bars.
Read full analysis →
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The E.T.👽 workshop submission is finally open! We aim at promoting scientific theory building in deep representation learning 2 tracks: 1p storyline & regular 14p full paper Deadline: Aug 1st 🖥️ empiricaltheory.github.io 📜 openreview.net/group?id=the... Let's do science! @eccv…

ECCV 2026 Workshop ET | OpenReview openreview.net
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↻ David Picard reposted
Shubhendu Trivedi @shubhendu.bsky.social

Don't know the author, but have become quite a fan of her work. Is always quite cool and original (sometimes conceptually, sometimes in terms of theoretical machinery &c.) arxiv.org/abs/2407.02458

Statistical Advantages of Oblique Randomized Decision Trees and Forests arxiv.org
AI Weekly's analysis →
  • Eliza O'Reilly's paper proves oblique Mondrian forests achieve minimax optimal convergence rates on ridge-function data where axis-aligned trees cannot.
  • For general ridge functions, no weighting of axis-aligned splits can match the rate oblique splits obtain, regardless of covariate distribution.
  • The analysis uses random tessellation theory from stochastic geometry, tying convergence to the relevant feature subspace rather than ambient dimension.
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↻ David Picard reposted
@si-cv-graphics.bsky.social

𝗦𝘂𝗿𝗳𝗹𝗼: 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝟯𝗗 𝗦𝘂𝗿𝗳𝗮𝗰𝗲 𝗙𝗹𝗼𝘄 𝗠𝗼𝗱𝗲𝗹 𝘄𝗶𝘁𝗵 𝗚𝗹𝗼𝗯𝗮𝗹 𝗦𝘁𝗮𝘁𝗲 Antoine Guédon, Shu Nakamura, Nicolas Dufour ... Angjoo Kanazawa arxiv.org/abs/2606.13644 Trending on scholar-inbox.com

Surflo: Consistent 3D Surface Flow Model with Global State arxiv.org
AI Weekly's analysis →
  • Surflo encodes a variable number of unposed RGB views into a fixed global state of K=128 tokens, then decodes 3D surface points via flow-matching ODEs.
  • From a single encoder pass the model can sample any number of oriented surface points, up to roughly one million, without committing to a fixed grid.
  • The authors report state-of-the-art results across eight benchmarks, including a Tanks and Temples Chamfer Distance of 0.0056 and F1 of 86.40 on 8 views.
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1/ MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and efficiency. Wed, Jul 8, 2026 • 10:30 AM – 12:15 PM KST 📜 arxiv.org/abs/2510.25897 🖥️ nicolas-dufour.github.io/miro/ 🧬 huggingface.co/nicolas-dufo... With @arrijitghosh.bsky.social and @lucasdegeorge.bsky.so…

nicolas-dufour/miro · Hugging Face huggingface.co
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Their own posts

Recent commentary

Interestingly, I have no idea if the training of this generative model works or not 😅

View on Bluesky · ♥ 15 ↻ 0 ↩ 2 · 38d ago
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In David Picard's orbit

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