New paper from the lab led by Fabian Mikulasch. 📄 https://arxiv.org/abs/2609.37789 🌐 https://info-ldm.github.io/ Code: https://github.com/fmi-basel/identifiable-stochastic-nuisance Why does predicting in latent space (JEPA, CPC, SimCLR…) work so well on messy data, with changing lighting, camera angles,Continue reading
Tag: ssl
The Lab at the Bernstein Conference
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
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
The lab at NeurIPS 2023
We’re at NeurIPS with two papers this year. If you are in New Orleans, come to see us! Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity. Rossbroich, J. and Zenke, F. (2023) doi:Continue reading
Paper: Implicit variance regularization in non-contrastive SSL
New paper from the lab accepted at NeurIPS: “Implicit variance regularization in non-contrastive SSL.” In our article, first-authored by Manu and Axel, we add further understanding to how non-contrastive self-supervised learning (SSL) methods avoid collapse.Continue reading