Reposted by @gabriben.bsky.socialEmma Robinson @OHBM 2026 @metricsemma.bsky.social · 26/04/2025Come see our poster #61 at 3pm @iclr-conf.bsky.social #iclr2025 surface deep learning for generalising fMRI decoding across different human brains with @dahansimon.bsky.social @gabriben.bsky.social 074 0
gabriben.bsky.social @gabriben.bsky.social · 24/04/2025Doing a demo on evaluating small language models for non-reasoning tasks (e.g. professionalize, shorten) at the amazon booth at 14:00 tomorrow at #ICLR 🇸🇬 001 0
gabriben.bsky.social @gabriben.bsky.social · 14/02/2025This could allow to create personalised simulations of brain function for, say, visual impaired patients. [3/3] 000 0
gabriben.bsky.social @gabriben.bsky.social · 14/02/2025The popular aspirational goal in the field is to represent, in real time, what people are thinking as a movie. In this paper, we go beyond the fMRI -> image paradigm. With video+audio <-> fMRI we also predict which brain regions are activated across individuals. [2/3] 100 0
gabriben.bsky.social @gabriben.bsky.social · 14/02/2025Excited to announce that our paper SIM: Surface-based fMRI Analysis for Inter-Subject Multimodal Decoding from Movie-Watching Experiments got accepted @iclr_conf 🎉 arxiv.org/abs/2501.16471 [1/3]arxiv.orgSIM: Surface-based fMRI Analysis for Inter-Subject Multimodal Decoding from Movie-Watching ExperimentsCurrent AI frameworks for brain decoding and encoding, typically train and test models within the same datasets. This limits their utility for brain computer interfaces (BCI) or neurofeedback, for whi... 100 0
Reposted by @gabriben.bsky.socialSimon Dahan @dahansimon.bsky.social · 24/01/2025[1/5] I'm very happy to share that our latest paper: “SIM: Surface-based fMRI Analysis for Inter-Subject Multimodal Decoding from Movie-Watching Experiments”, has been accepted to #ICLR2025! 🎉 163 0
