true that

What about the DJ Kicks from Kruder and Dorfmeister ?
Arf... seems from 1996....

Following the success of the EurIPS and NeurIPS-Mexico City pilots in 2025, we are thrilled to announce two official NeurIPS 2026 satellite events for this year!

These will be held in Paris, France and Atlanta, USA, respectively, running alongside the main venue in Sydney, Australia.

saddest thing i've read today

I see Sirat in poster... I hope this was your choice (assuming those are photos of a Cinema) !

We are recruiting four positions connected to Machine Learning, Statistical Learning, and AI for Science in the Applied Mathematics department at École polytechnique. Join our vibrant community at IP Paris and Hi! Paris IA center. List below🧵 tinyurl.com/3jpw9t26

Maybe it is also LLM generated ?

"On meurt dans mon Université", par la présidente de l'Université Paul Valéry à Montpellier. Parce que les baisses de financement des universités ce sont aussi des conditions de travail si dégradées qu'elles en deviennent intenables

beware of the abstract submission deadline !

North Brittany ?

🏹 Job alert: 3 PhD positions in AI, Earth Observation, and Science-Policy interface

📍 Vannes 🇫🇷 & Ispra 🇮🇹
📅 Apply by Jan 15th
🔗 https://www-obelix.irisa.fr/job-offers/

One of those internships is on Gromov $\delta$-hyperbolicity for GNNs, and will be cosupervised together with Nicolas, myself and Laetitia Chapel. Take a look and spread the words !

Halloween après l'heure

so true....

#Distinction 🏆| Charlotte Pelletier, lauréate d'une chaire #IUF, développe des méthodes d’intelligence artificielle appliquées aux séries temporelles d’images satellitaires.
➡️ www.ins2i.cnrs.fr/fr/cnrsinfo/...
🤝 @irisa-lab.bsky.social @cnrs-bretagneloire.bsky.social

I love how they managed to distill the spirit of the first Alien movie. And the Eye creature .... !

Huge fan too. Brace yourself for episode 5 ^^

Trying hard to decouple my interest for the scientific questions behind AI and this....😮‍💨

Abstract: Under the banner of progress, products have been uncritically adopted or even imposed on users — in past centuries with tobacco and combustion engines, and in the 21st with social media. For these collective blunders, we now regret our involvement or apathy as scientists, and society struggles to put the genie back in the bottle. Currently, we are similarly entangled with artificial intelligence (AI) technology. For example, software updates are rolled out seamlessly and non-consensually, Microsoft Office is bundled with chatbots, and we, our students, and our employers have had no say, as it is not considered a valid position to reject AI technologies in our teaching and research. This is why in June 2025, we co-authored an Open Letter calling on our employers to reverse and rethink their stance on uncritically adopting AI technologies. In this position piece, we expound on why universities must take their role seriously toa) counter the technology industry’s marketing, hype, and harm; and to b) safeguard higher education, critical thinking, expertise, academic freedom, and scientific integrity. We include pointers to relevant work to further inform our colleagues.

Figure 1. A cartoon set theoretic view on various terms (see Table 1) used when discussing the superset AI (black outline, hatched background): LLMs are in orange; ANNs are in magenta; generative models are in blue; and finally, chatbots are in green. Where these intersect, the colours reflect that, e.g. generative adversarial network (GAN) and Boltzmann machine (BM) models are in the purple subset because they are both generative and ANNs. In the case of proprietary closed source models, e.g. OpenAI’s ChatGPT and Apple’s Siri, we cannot verify their implementation and so academics can only make educated guesses (cf. Dingemanse 2025). Undefined terms used above: BERT (Devlin et al. 2019); AlexNet (Krizhevsky et al. 2017); A.L.I.C.E. (Wallace 2009); ELIZA (Weizenbaum 1966); Jabberwacky (Twist 2003); linear discriminant analysis (LDA); quadratic discriminant analysis (QDA).

Table 1. Below some of the typical terminological disarray is untangled. Importantly, none of these terms are orthogonal nor do they exclusively pick out the types of products we may wish to critique or proscribe.

Protecting the Ecosystem of Human Knowledge: Five Principles

Finally! 🤩 Our position piece: Against the Uncritical Adoption of 'AI' Technologies in Academia:
doi.org/10.5281/zeno...

We unpick the tech industry’s marketing, hype, & harm; and we argue for safeguarding higher education, critical
thinking, expertise, academic freedom, & scientific integrity.
1/n

I always appreciate @cwarzel.bsky.social's takes on AI!

👀

Distributional Reduction paper with H. Van Assel, @ncourty.bsky.social, T. Vayer , C. Vincent-Cuaz, and @pfrossard.bsky.social is accepted at TMLR. We show that both dimensionality reduction and clustering can be seen as minimizing an optimal transport loss 🧵1/5. openreview.net/forum?id=cll...

A subtle combination of the three ? With a pinch of regrets about what could have been perfected ?

We have been reworking the Quickstart guide of POT to show multiple examples of OT with the unified API that facilitates access to OT value/plan/potentials. It allows to select regularization/unbalancedness/lowrank/Gaussian OT with just a few parameters. pythonot.github.io/master/auto_...

maybe, in the end, you are the innie @chriswolfvision.bsky.social ?

Solutions to the PAWL problem in 1D for different amounts of mass to be transported

⚔️ One for all and all for one ⚔️
Efficient computation of PArtial Wasserstein distances on the Line (PAWL)

is accepted to @iclr-conf.bsky.social

Joint work with Laetitia Chapel: we introduce an 𝑂(𝑛 𝑙𝑜𝑔 𝑛) solver for partial Optimal Transport (OT) in 1D

openreview.net/forum?id=kzE...

🧵 1/2

Slicing Unbalanced Optimal Transport

Clément Bonet, Kimia Nadjahi, Thibault Sejourne, Kilian FATRAS, Nicolas Courty

Action editor: Benjamin Guedj

https://openreview.net/forum?id=AjJTg5M0r8

#transport #outliers #optimal

Multisample Flow Matching: Straightening Flows with Minibatch Couplings Simulation-free methods for training continuous-time generative models construct probability paths that go between noise distributions and individual data samples. Recent works, such as Flow Matching,...

Nice ! But no all the flow matching methods rely on an independent coupling, some use mini-batch OT also, see e.g. arxiv.org/abs/2304.14772 or arxiv.org/abs/2302.00482

a little girl in a red and purple dress is holding a purple toy and making a funny face . ALT: a little girl in a red and purple dress is holding a purple toy and making a funny face .

Very grumpy here. Me ACing ICLR and reading the reviews