Like every year, the lab is excited to attend and present at the Bernstein Conference and Satellite workshops. Workshop talks Monday 28th, 15:05-15:35 (room 3.104) Rory will give the talk “Connectome-constrained spiking network models ofContinue reading
Tag: self-supervised learning
Paper: Understanding Self-Supervised Learning via Latent Distribution Matching
Fabian’s paper “Understanding Self-Supervised Learning via Latent Distribution Matching” was accepted as ICML spotlight! https://arxiv.org/abs/2605.03517 We unify self-supervised learning (SSL) algorithms (e.g., contrastive, VICReg, stopgrad) via latent distribution matching (LDM), which matches an induced latentContinue reading
Paper: Latent Predictive Learning
Our paper “The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks” is now published in Nature Neuroscience. https://www.nature.com/articles/s41593-023-01460-y Sensory networks in our brain represent environmental objects as points onContinue reading
Hebbian plasticity could be the brain’s trick to make self-supervised learning work
Please take a look at our new preprint “The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks.” https://biorxiv.org/cgi/content/short/2022.03.17.484712v2 In this work led by Manu Srinath Halvagal we argue thatContinue reading