9 citations · 32 across the 14 of their papers we have counts for
7 papers · 1 filter
Deep ReLU Networks Can Fail the Best Approximation Property for Continuous Target Functions
Sebastian Kassing
We construct a continuous function on for which the infimum of the -approximation error over the class of ReLU networks with two hidden layers and architecture $(2,…
The Role of Target Update Frequencies in Q-Learning
Simon Weissmann, Tilman Aach, Benedikt Wille +2
The target network update frequency (TUF) is a central stabilization mechanism in (deep) Q-learning. However, their selection remains poorly understood and is often treated merely…
An Approximate Ascent Approach To Prove Convergence of PPO
Leif Doering, Daniel Schmidt, Moritz Melcher +4
Proximal Policy Optimization (PPO) is among the most widely used deep reinforcement learning algorithms, yet its theoretical foundations remain incomplete. Most importantly, conver…
Stochastic Modified Flows for Riemannian Stochastic Gradient Descent
Benjamin Gess, Sebastian Kassing, Nimit Rana
We give quantitative estimates for the rate of convergence of Riemannian stochastic gradient descent (RSGD) to Riemannian gradient flow and to a diffusion process, the so-called Ri…
On the existence of optimal shallow feedforward networks with ReLU activation
Steffen Dereich, Sebastian Kassing
We prove existence of global minima in the loss landscape for the approximation of continuous target functions using shallow feedforward artificial neural networks with ReLU activa…
On minimal representations of shallow ReLU networks
S. Dereich, S. Kassing
The realization function of a shallow ReLU network is a continuous and piecewise affine function , where the domain is partitioned by a…
Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes
Steffen Dereich, Sebastian Kassing
In this article, we consider convergence of stochastic gradient descent schemes (SGD), including momentum stochastic gradient descent (MSGD), under weak assumptions on the underlyi…