Quentin Bertrand
@quentinbertrand.bsky.social
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📥 34
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Machine Learning Researcher at Inria
reposted by
Quentin Bertrand
Tony S.F.
9 days ago
Big lack of qualified reviewers? The cost of generation seems headed to 0 relative to the cost of verification; seems obvious that we must change norms so verification is seen as more of a contribution? What if we require authors to review for some number of conferences before being able to submit?
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Let's give proper credit to the
@sli.dev
GOAT:
@remiemonet.bsky.social
add a skeleton here at some point
8 days ago
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We had a blast discussing memorization, generalization, and beyond for diffusion models at our @icml tutorial with
@mathurinmassias.bsky.social
! Slides & material:
memorization-generalization.github.io
8 days ago
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reposted by
Quentin Bertrand
Nicolas Dufour
9 days ago
I'm sadly not at ICML, but
@arrijitghosh.bsky.social
and
@lucasdegeorge.bsky.social
are presenting MIRO right now! Happening right now at poster board 2508!
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reposted by
Quentin Bertrand
Tony S.F.
10 months ago
My paper on Generalized Gradient Norm Clipping & Non-Euclidean (L0, L1)-Smoothness (together with collaborators from EPFL) was accepted as an oral at NeurIPS! We extend the theory for our Scion algorithm to include gradient clipping. Read about it here
arxiv.org/abs/2506.01913
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reposted by
Quentin Bertrand
Mathurin Massias
10 months ago
Our work on the generalization of Flow Matching got an oral at Neurips ! Go see
@quentinbertrand.bsky.social
present it there :)
add a skeleton here at some point
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reposted by
Quentin Bertrand
Mathurin Massias
about 1 year ago
New paper on the generalization of Flow Matching
www.arxiv.org/abs/2506.03719
🤯 Why does flow matching generalize? Did you know that the flow matching target you're trying to learn *can only generate training points*? w
@quentinbertrand.bsky.social
@annegnx.bsky.social
@remiemonet.bsky.social
👇👇👇
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What an amazing week with insightful discussions and interactions!
@franceausenegal.bsky.social
add a skeleton here at some point
over 1 year ago
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reposted by
Quentin Bertrand
Olivier Grisel
over 1 year ago
Recently merged in scikit-learn's main branch: display the maximum predicted class probability in 2D continuous feature spaces (mostly for didactic purposes):
scikit-learn.org/dev/auto_exa...
The linked example has been updated to include some conclusions we can draw from this plot.
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reposted by
Quentin Bertrand
Rémi Emonet
over 1 year ago
Visit the playground at the end of our blog post (with co-authors
@annegnx.bsky.social
, Ségolène Martin,
@mathurinmassias.bsky.social
,
@quentinbertrand.bsky.social
)
dl.heeere.com/cfm#cfm-play...
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reposted by
Quentin Bertrand
Gaël Varoquaux
over 1 year ago
👩🎓👨🎓 Internship offers (1st step to PhD program) in my group:
team.inria.fr/soda/job-off...
Topics: ◼ Health AI & causality, accounting for censoring (for people who love health impact) ◼ Foundation models for tabular learning (for people into bigger models) Come work with us!
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This blog post provides intuition and nice illustrations to understand normalizing flows and flow matching techniques! w.
@annegnx.bsky.social
, Ségolène Martin,
@mathurinmassias.bsky.social
, and
@remiemonet.bsky.social
(the king for figures)
add a skeleton here at some point
over 1 year ago
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Very cool ref! Did not know about it!
add a skeleton here at some point
over 1 year ago
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reposted by
Quentin Bertrand
Valentin De Bortoli
over 1 year ago
Nice blogpost and very cool illustrations 😍. I will die on the hill that most of the FM ideas where introduced back in 2021 by Stefano Peluchetti in his underappreciated paper
openreview.net/forum?id=oVf...
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reposted by
Quentin Bertrand
Mathurin Massias
over 1 year ago
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
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