4 papers · 1 filter
Is Variational Monte Carlo Robust? Sharp Moment Thresholds and Heavy-tailed Stochastic Optimization
Philipp Grohs, Davide Nobile
Variational Monte Carlo (VMC) is a central algorithm in electronic structure theory and has gained renewed importance through modern neural-network ansätze such as FermiNet. At its…
Robust and Fast Training via Per-Sample Clipping
Davide Nobile, Philipp Grohs
We propose a robust gradient estimator based on per-sample gradient clipping and analyze its properties both theoretically and empirically. We show that the resulting method, per-s…
The Information-Theoretic Benefit of Shared Representations under Orthogonality Constraints
Thomas Dittrich, Oliver Potocki, Philipp Grohs
Modern deep learning architectures are increasingly multi-task and multi-modal, using a pretrained foundation model combined with task-specific, fine-tuned models. Empirically, exp…
Limitations of Learning Tanh Neural Networks with Finite Precision
Philipp Grohs, Matěj Trödler
We investigate limitations of learning neural networks from point evaluations under finite-precision computations and accuracy guarantees, building on Berner, Grohs,…