activity
20132026
most citedTowards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't

50 citations · 193 across the 27 of their papers we have counts for

collaborators
Showing 2024Show all

3 papers · 1 filter

math.OC2024

Nesterov acceleration in benignly non-convex landscapes

Kanan Gupta, Stephan Wojtowytsch

While momentum-based optimization algorithms are commonly used in the notoriously non-convex optimization problems of deep learning, their analysis has historically been restricted…

math.AP2024

A note on spatially inhomogeneous Cahn-Hilliard energies

Stephan Wojtowytsch

In 2023, Cristoferi, Fonseca and Ganedi proved that Cahn-Hilliard type energies with spatially inhomogeneous potentials converge to the usual (isotropic and homogeneous) perimeter…

cs.LG2024★ 1 cited

SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations

Xuan Zhang, Jacob Helwig, Yuchao Lin +4

We consider using deep neural networks to solve time-dependent partial differential equations (PDEs), where multi-scale processing is crucial for modeling complex, time-evolving dy…