🏢 Institute of Science and Technology Austria

I-OBS, a novel family of sparse recovery algorithms leveraging second-order information, achieves faster convergence rates for sparse DNNs, validated by large-scale experiments.

AI for science faces hidden biases in causal inference; this paper reveals these flaws using ant behavior data, introducing ISTAnt benchmark, and provides guidelines for more accurate causal AI.

Deep neural collapse, previously believed optimal, is shown suboptimal in multi-class, multi-layer networks due to a low-rank bias, yielding even lower-rank solutions.

Optimal matrix denoising with doubly heteroscedastic noise achieved!

This study marries causal representation learning with dynamical systems to enable parameter identification in real-world scientific data, unlocking downstream causal analysis for various applications…