Statistical and machine learning research team, @inria and @Univ_CotedAzur, part of @LjadNice and @Laboratoire_I3S, located in Sophia-Antipolis, Nice (France).
It is still possible to submit to @GeMSS24 (Generative Modeling Summer School 2024)! The deadline has been postponed until Wednesday (April 3, 2024), 23:59 (Central European Summer Time).
I am super excited about the GeMSS summer school on generative AI we kick off tomorrow! Fantastic invited lecturers and co-organised with the two amazing @pamattei and @jmtomczak!
https://t.co/YPpyaiQrpT
@DataScienceDK @AiCentreDK@InriaMaasai@CogSys@DTUtweet @TUeindhoven
Still time to apply for this👩🎓Ph.D. position! If you are a Master student in Data Science with interest in #AI, #NLP and #StatisticalLearning, have a look at the offer! ⬇️⬇️⬇️
Excited to present my article on safe semi-supervised learning at #ICLR2023 (Poster session 4, Tue 2, 4:30 pm)! Even though SSL received a lot of attention in the past years, most methods do not benefit from theoretical guarantees. (1/8) #SemiSupervisedLearning
Still a few days to apply to the #GeMSS23 summer school in Copenhagen and learn about deep generative models (VAEs, diffusion models, prob circuits, flows, GANs…) and applications (biology, causality, missing data…)!
The deadline to #GeMSS23 is now April 14, 2023 (EOD)! Don't wait, apply to this amazing summer school on #GenerativeAI!
🤩An additional update, we have two new lecturers:
Prof. Ole Winther @OleWinther1 and Prof. Wouter Boomsma @WouterBoomsmaDK! 🤩
Open (fully funded)👩🎓Ph.D. position on « #AI models hybridizing #machineLearning and #argumentation to counter online misinformation and cyberbullying » with @serena_villata at @3IAcotedazur!
➡️More information on: https://t.co/gxDVKYnTgf
⚠️Deadline for application: 15 may
#NeurIPS2022 is starting today: our @InriaMaasai team will present there 3 papers on GNNs, discriminative clustering and interpretability:
➡️https://t.co/vYD1QgJCdN
➡️https://t.co/Rj8PKCQRMR
➡️https://t.co/JUN6uNxEHF
Mateo Espinosa Zarlenga, @pietro_barbiero & @CiraGabriele will describe concept-embedding models, a new self-explainable deep architecture that does not sacrifice accuracy for explainations (https://t.co/yf46zeoPIn)
https://t.co/mBH2ztP518
Beyond the accuracy-explainability tradeoff with "Concept Embedding Models" from #NeurIPS (https://t.co/yZKYvJX2Fa): moving towards trustworthy #AI!
Light reading mode: https://t.co/UwzTsIYySs
@louis_ohl will show how to train standard neural nets without labels (!) using a new generalisation of the mutual information (https://t.co/pcrZp8Qnhn), w/ @pamattei@cbouveyron@warith_h & Fred Precioso
https://t.co/mPKAhtIq76
Very proud to have presented my work highlighted at #EANM22 on the prediction of the lung cancer response to immunotherapy. We show that heterogeneous biomarkers can be combined to improve predictive performance. @officialEANM @Lacassagne_Nice @3IAcotedazur @InriaMaasai
Very excited for this workshop on lifelong-continual learning (Dec 12 in Hyderabad India). Our goal is to bring together the community from the Asian region.
Submit your work (due end of October).
RT appreciated. Help us spread the word!
[#MathC2+] Jour 1 Grand merci 👏aux 👩🎓👨🎓doctorant-e-s des équipes de recherche du centre @Inria : Aude @InriaMaasai Aline @wimmics Clotilde (Biocore) Paul (Calisto) Bernard (Diana) Yingyu @InriaEpione & Owen qui ont présenté leur parcours/leur sujet et échangé avec les 50 lycéens
I'm really excited to announce that my paper with A. Fresse, @Marco_Corneli and @cbouveyron, on the Dynamic Latent Block Model with an application to pharmacovigilance is now published on Statistics and Computing, also available on HAL at: https://t.co/BWVAFdt1gY.🥳
We'll have two papers at #ICLR2022 next week! @NielsIpsen will discuss ways of handling missing values in supervised deep learning (https://t.co/0h0TJx0YBo) at session 3, and @CedricCuaz will introduce the semi-relaxed Gromov-Wasserstein divergence at session 1, see thread⬇️
How to get a factorization model sensitive to hierarchical partitioning on graphs, for most unsupervised tasks you can imagine? Use the semi-relaxed Gromov-Wasserstein divergence! #OptimalTransport
Meet me at the Poster session 1 at #iclr2022 next Monday to discuss about it 😉