2 citations · 2 across the 1 of their papers we have counts for
29 papers
Symbolic Recovery of Differential Equations: The Identifiability Problem
Philipp Scholl, Aras Bacho, Holger Boche +1
Symbolic recovery of differential equations is the ambitious attempt at automating the derivation of governing equations with the use of machine learning techniques. In contrast to…
When is a System Discoverable from Data? Discovery Requires Chaos
Zakhar Shumaylov, Peter Zaika, Philipp Scholl +3
The deep learning revolution has spurred a rise in advances of using AI in sciences. Within physical sciences the main focus has been on discovery of dynamical systems from observa…
Uncertainty Quantification for Regression: A Unified Framework based on kernel scores
Christopher Bülte, Yusuf Sale, Gitta Kutyniok +1
Regression tasks, notably in safety-critical domains, require reliable uncertainty quantification, yet the literature remains largely classification-focused. To address this, we in…
An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression
Christopher Bülte, Yusuf Sale, Timo Löhr +3
Uncertainty quantification is crucial in machine learning, yet most (axiomatic) studies of uncertainty measures focus on classification, leaving a gap in regression settings with l…
A Variational Framework for the Complexity of PDE Solutions
Juan Esteban Suarez Cardona, Holger Boche, Gitta Kutyniok
Partial Differential Equations (PDEs) are fundamental mathematical models for describing physical phenomena, yet most PDEs of practical interest require numerical approximations. T…
Conflicting Biases at the Edge of Stability: Norm versus Sharpness Regularization
Maria Matveev, Vit Fojtik, Hung-Hsu Chou +2
The remarkable generalization properties of overparameterized networks are often attributed to implicit biases, such as norm minimization at small learning rates and low sharpness…