Je vous annonce la fin du monde... Ah ! tout le monde va périr !

For a terrapin, the back of a hippo is an ideal spot to catch some sun

“We’re all going to the beach” #janeygodleyvoiceover

Red Hot 🌶️ #ASMST

Ever wondered how to carry data augmentation depending on the data label? That's what we've investigated in our #iclr2022 paper w/ Moreau Thomas, J. Paillard & Alexandre Gramfort CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG signals 📄 arxiv.org/abs/2106.13695 🧵1/6

Our paper on data augmentation for EEG data was accepted by the Journal of Neural Engineering! It all started w the nice work done by Joseph during his internship Parietal, INRIA! Congrats to him & our amazing co-authors Moreau Thomas & Alexandre Gramfort! Short 🧵 on our findings 👇!

To preprocess or not to preprocess your #EEG (when building #ML models) 🔮? 💫We are thrilled to share our latest #preprint, studying the challenge of learning brain-specific biomarkers from EEG 🧠📶 using #ML ⚙️🖥️: biorxiv.org/content/10.110… We compile arguments and evidence from

🧠 Introducing GREEN (Gabor Riemann EEG Net), a new modular neural network architecture for #EEG analysis. GREEN combines the power of wavelet transforms 〰️ and Riemannian geometry 🌐 to learn sparse and interpretable representations of EEG data. It's designed with modularity in

Would you have thought that object recognition CNNs can encode information on object number across changes in identity and context? New preprint, with Thomas Chapalain and @BertrandThirion, on visual #number_sense: biorxiv.org/content/10.110…

✨📰preprint - From prediction to prescription: Machine learning and Causal Inference hal.science/hal-04774700 Machine learning for individual treatment effect Didactic and precise, bridging statistical, computational, and epidemiological thinking. #statstwitter #epitwitter 1/5