Who's Who of AI

David Picard

178 trust researcher @davidpicard.eurosky.social · 2,888 followers
AI research Vision & synthetic media Compute & infrastructure
Why they matter

Researcher with public evidence across AI research, Vision & synthetic media, Compute & infrastructure.

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past 30d
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past 30d
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2d ago
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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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↻ David Picard reposted
@neuripseurope.bsky.social

A gentle reminder, the suggested deadline for the NeurIPS 2026 workshops is close (Aug 29th)! 28 workshops have been accepted for the Paris event (Sat Dec 12 + Sun Dec 13): blog.neurips.cc/2026/08/10/a...

Announcing the NeurIPS 2026 Workshops – NeurIPS Blog blog.neurips.cc
AI Weekly's analysis →
  • NeurIPS 2026 accepted 102 workshops from 454 valid submissions, spread across Sydney, Paris, and Atlanta venues.
  • Acceptance rates were 21.5% in Sydney, 25.4% in Paris, and 23.6% in Atlanta, with each proposal getting at least two reviews.
  • New safeguards cap workshops at 8 organizers and bar any individual from appearing on more than 2 proposals.
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↻ David Picard reposted
Diane Larlus @dlarlus.bsky.social

We just released our #ECCV2026 paper on Model Merging for Computer Vision 🎓 arxiv.org/abs/2604.12935 Joint work w @pdejorge.bsky.social Cesar De Souza @bjoernmichele.bsky.social @mbsariyildiz.bsky.social @weinzaepfelp.bsky.social Florent Perronnin & @skamalas.bsky.social See P…

Task Alignment: A Simple Proxy for Practical Model Merging Across Diverse Vision Tasks arxiv.org
AI Weekly's analysis →
  • A NAVER Labs Europe paper accepted at ECCV 2026 introduces a 'task alignment proxy' for selecting model-merging hyperparameters without training the decoder for each candidate.
  • The method targets heterogeneous vision tasks with trainable decoders, moving beyond the CLIP image classification setup that dominated prior model merging work.
  • The authors claim the proxy speeds up hyperparameter selection by orders of magnitude while retaining downstream performance.
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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.
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View on Bluesky · ♥ 26 ↻ 6 ↩ 2 · 2 from the directory shared this · 80d ago
↻ David Picard reposted
Simon Willison @simonwillison.net

New TIL on using Blender with coding agents on macOS: til.simonwillison.net/llms/blender... GPT-6 Astra (medium): > Use the already install /Applications/Blender to render a scene of a pelican riding a bicycle > OK add a background and a lot of flair > OK make it a whole lot b…

Using Blender with coding agents on macOS til.simonwillison.net
AI Weekly's analysis →
  • Simon Willison used ChatGPT's macOS Codex mode with GPT-6 Astra (Medium) to have Blender render a pelican riding a bicycle.
  • Three iterative prompts — render, add flair, make it better — took 2m39s, 3m51s, and 5m59s respectively.
  • Codex generated a reusable 'Blender Local' Markdown skill so subsequent scene requests can invoke Blender in one line.
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↻ David Picard reposted
Christian Wolf @chriswolfvision.bsky.social

New papers on recurrent transformers: arxiv.org/pdf/2606.21562 (ours): train full Tr, distill -> rec Tr arxiv.org/pdf/2608.02870 train full and rec jointly, min. coherence arxiv.org/pdf/2606.06479 train rec Tr with predictive obj. arxiv.org/pdf/2608.08888 train full Tr, add re…

arxiv.org View on Bluesky →
↻ David Picard reposted
Christian Wolf @chriswolfvision.bsky.social

New papers on recurrent transformers: arxiv.org/pdf/2606.21562 (ours): train full Tr, distill -> rec Tr arxiv.org/pdf/2608.02870 train full and rec jointly, min. coherence arxiv.org/pdf/2606.06479 train rec Tr with predictive obj. arxiv.org/pdf/2608.08888 train full Tr, add re…

arxiv.org View on Bluesky →
↻ David Picard reposted
Christian Wolf @chriswolfvision.bsky.social

New papers on recurrent transformers: arxiv.org/pdf/2606.21562 (ours): train full Tr, distill -> rec Tr arxiv.org/pdf/2608.02870 train full and rec jointly, min. coherence arxiv.org/pdf/2606.06479 train rec Tr with predictive obj. arxiv.org/pdf/2608.08888 train full Tr, add re…

arxiv.org View on Bluesky →
↻ David Picard reposted
Christian Wolf @chriswolfvision.bsky.social

New papers on recurrent transformers: arxiv.org/pdf/2606.21562 (ours): train full Tr, distill -> rec Tr arxiv.org/pdf/2608.02870 train full and rec jointly, min. coherence arxiv.org/pdf/2606.06479 train rec Tr with predictive obj. arxiv.org/pdf/2608.08888 train full Tr, add re…

arxiv.org View on Bluesky →

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
View on Bluesky · ♥ 8 ↻ 4 ↩ 0 · 2 from the directory shared this · 62d ago
↻ David Picard reposted
@fyavuz1.bsky.social

Excited to share our work, IDeaL! Big thanks to @mbsariyildiz.bsky.social and @dlarlus.bsky.social for the guidance and support from day one. At ECCV? We're in Poster Session 2, Thu Sep 10. Come say hi! Paper: arxiv.org/abs/2608.24759 Project page: blisgard.github.io/ideal_pro…

IDeaL: Data-Free Multi-Teacher Distillation via Improved Dead Leaves arxiv.org
AI Weekly's analysis →
  • IDeaL generates teacher-specific synthetic samples using decorrelation losses at patch and image levels, removing the need for the teachers' original training images.
  • With a 1K-image budget, students trained on IDeaL samples match or surpass those trained on a 1K-image subset of ImageNet.
  • The paper, by Feyza Yavuz, Mert Bülent Sarıyıldız and Diane Larlus, is accepted at ECCV 2026.
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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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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 · 98d ago

During my vacation, I was staying at a (lovely old school) hotel that had this amazing AI slop deck of cards in one of the rooms. The more you look at it, the more it's uncanny. I should have asked to buy it because those will be the artefacts of our time. In 10y you won't be able to recognize them.

View on Bluesky · ♥ 11 ↻ 0 ↩ 2 · 47d ago

J'étais là, Gandalf, quand la station MIR devait s'écraser sur Paris. Bon, en vrai, je chargeais des camions dans un entrepôt pour gagner un peu de thunes. On est sorti regarder. J'avais que 2 paires de lunettes de soleil et j'y ai gagné un tout petit point gris au milieu de ma vision. Mais cool!

View on Bluesky · ♥ 2 ↻ 0 ↩ 1 · 26d ago
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