🏢 Technical University of Denmark

SLU: a novel, low-memory uncertainty score for neural networks, achieves logarithmic memory scaling with model parameters, providing well-calibrated uncertainties and outperforming existing methods.

Bayesian neural networks often underfit due to their lack of reparameterization invariance; this paper introduces a Riemannian diffusion process to improve posterior sampling and enhance predictive pe…

This research presents novel adjoint methods for efficiently differentiating Lanczos and Arnoldi iterations, unlocking accurate gradients for large-matrix functions in machine learning.

Equivariant Neural Diffusion (END) revolutionizes 3D molecule generation with a learnable forward process, achieving state-of-the-art results and enhanced controllability.