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 it…
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, MatÄj Trödler
We investigate limitations of learning neural networks from point evaluations under finite-precision computations and accuracy guarantees, building on Berner, Grohs,…
Theory-to-Practice Gap for Neural Networks and Neural Operators
Philipp Grohs, Samuel Lanthaler, Margaret Trautner
This work studies the sampling complexity of learning with ReLU neural networks and neural operators. For mappings belonging to relevant approximation spaces, we derive upper bound…