Professor, Institute of Science and Technology Austria
2 papers at NeurIPS 2025
We prove that neural collapse is approximately optimal in deep regularized ResNets and transformers end-to-end.
A method for continually and privately estimating both the first and second moments of a data stream with reduced noise compared to naive approaches.
