7 papers
Convergence theory for Hermite approximations under adaptive coordinate transformations
Yahya Saleh
Recent work has shown that parameterizing and optimizing coordinate transformations using normalizing flows, i.e., invertible neural networks, can significantly accelerate the conv…
Enhancing polynomial approximation of continuous functions by composition with homeomorphisms
Álvaro Fernández Corral, Yahya Saleh
We enhance the approximation capabilities of algebraic polynomials by composing them with homeomorphisms. This composition yields families of functions that remain dense in the spa…
Sound Signal Synthesis with Auxiliary Classifier GAN, COVID-19 cough as an example
Yahya Sherif Solayman Mohamed Saleh, Ahmed Mohammed Dabbous, Lama Alkhaled +3
One of the fastest-growing domains in AI is healthcare. Given its importance, it has been the interest of many researchers to deploy ML models into the ever-demanding healthcare do…
Taylor-mode automatic differentiation for constructing molecular rovibrational Hamiltonian operators
Andrey Yachmenev, Emil Vogt, Álvaro Fernández Corral +1
We present an automated framework for constructing Taylor series expansions of rovibrational kinetic and potential energy operators for arbitrary molecules, internal coordinate sys…
Transferability and interpretability of vibrational normalizing-flow coordinates
Emil Vogt, Álvaro Fernández Corral, Yahya Saleh +1
The choice of vibrational coordinates is crucial for the accuracy, efficiency, and interpretability of molecular vibrational dynamics and spectra calculations. We explore the recen…
Bounds on the Generalization Error in Active Learning
Vincent Menden, Yahya Saleh, Armin Iske
We establish empirical risk minimization principles for active learning by deriving a family of upper bounds on the generalization error. Aligning with empirical observations, the…