Hugo Richard retweeted
The #IBC 3rd-release data-paper is out! We validate naturalistic tasks using #FastSRM, an unsupervised model where a shared response and individual maps are learnt jointly from fMRI time series. Check it out! doi.org/10.1038/s41597-024-0… @BertrandThirion @hr1ch3rd @NeuroSpin_91
Hugo Richard retweeted
Since I joined @CriteoAILab, I started digging into applied causality topics. Our recent work “Maximizing the Success Probability of Policy Allocations in Online Systems” got accepted to AAAI-24 🥳!
arxiv.org/abs/2312.16267
Ping @artembetley @RahierThibaud
🧵🧵🧵
Hugo Richard retweeted
Looking forward to present at #NeurIPS2023, NOLA & Paris, a unification framework for diffusion models and GANs.
It opens up the possibility to train generators with diffusion and GANs without generators!
📜 openreview.net/forum?id=HMhE…
🖥️ github.com/White-Link/gpm
Hugo Richard retweeted
The preprint on the #IBC-dataset 3rd-release is out! We showcase the application of #FastSRM to extract, from naturalistic stimuli, functional networks of the visual, auditory and language systems. Check it out! hal.science/hal-04272993 @BertrandThirion
Hugo Richard retweeted
New paper by @hr1ch3rd and @BertrandThirion offers a fast, memory efficient, and identifiable application of the shared response model doi.org/10.52294/001c.87954
[ICML 2023, Exhibit Hall 1, Today at 2 PM] On a scheduling problem, we learn hints about job sizes as more jobs are scheduled. We use hints to make better scheduling decision via a combination of bandits methods and worse case scheduling algorithm. 1/6
Hugo Richard retweeted
📢📢 New preprint: arxiv.org/abs/2305.15042
"Test like you Train in Implicit Deep Learning"
w. @PierreAblin @tomamoral @gabrielpeyre
We investigate whether implicit deep learning models benefit from more test-time inner iterations.
Hugo Richard retweeted
Following Elsevier's decision to raise the APC for NeuroImage to $3,450, all editors (inc. EiCs @fmrib_steve @tobergmann @BirteUta) from NeuroImage and NeuroImage:Reports have resigned, effective immediately. I am joining this action and have also resigned imaging-neuroscience.org/Ann…
ALT Resignation statement. See https://imaging-neuroscience.org/Announcement.pdf for full announcement.
Hugo Richard retweeted
C'est un honneur de recevoir le #SophIA Award 2022, et d'être intégrée et soutenue par @InriaStartup, @IncubatPacaEst, et @3IAcotedazur. Merci à Marco Lorenzi d'avoir collaboré avec nous, et nous avons hâte de poursuivre notre plan d'action cette année!
#SOPHIASUMMIT
🔴 En #direct de la cérémonie de clôture & remise des prix félicitations au projet lauréat "NeuroPin" du SophIA Award 2022 porté par @Antonia_Machlou !
Félicitations à l'entreprise @AtelierMy3D qui remporte le prix "Sustainability" attribué par @SAPLabsinFrance !
Hugo Richard retweeted
Our paper on using Optimal Transport to compare human cortical surfaces was accepted at #NeurIPS22 🥳
We implement a new OT solver (FUGW) to compare human brains although their anatomy and functional activity patterns differ a lot.
arxiv.org/abs/2206.09398
github.com/alexisthual/fugw
Hugo Richard retweeted
Thrilled to give an oral tomorrow at 10:30 at #OHBM22!
We successfully decode 50 cognitive processes from fMRI maps by exploiting the large amount of data in @Neurovault and leveraging the @CognitiveAtlas ontology
See you in M1 for the Modeling and Analysis Activation session!
Hugo Richard retweeted
Thrilled to give a demo brain-cockpit 🧠🚀, a web-based viewer @StanDehaene, @BertrandThirion and I have been working on to explore deep phenotyping fMRI data and individual alignments!
See you @OHBM friends in M1 at 13:00 ☺️
Hugo Richard retweeted
Now out in @ApertureOHBM! Our comprehensive overview, led by @manojneuro, of the @UseBrainIAK toolkit for advanced fMRI analysis apertureneuropub.cloud68.co/…
Excited to share our comprehensive overview of BrainIAK: The Brain Imaging Analysis Kit osf.io/db2ev/ via @OSFramework This work includes new example Jupyter notebooks for all methods in BrainIAK.