18 citations · 27 across the 6 of their papers we have counts for
9 papers
High-Frequency Pricing at Scale for E-Commerce
Stefan Birr, Tobias Huelden, Mones Raslan +7
This paper presents the design, development, and implementation of a specialized forecast-then-optimize algorithmic pricing tool for sales campaigns in fashion e-commerce. Sales ev…
Deep Learning based Forecasting: a case study from the online fashion industry
Manuel Kunz, Stefan Birr, Mones Raslan +13
Demand forecasting in the online fashion industry is particularly amendable to global, data-driven forecasting models because of the industry's set of particular challenges. These…
Expressivity of Deep Neural Networks
Ingo Gühring, Mones Raslan, Gitta Kutyniok
In this review paper, we give a comprehensive overview of the large variety of approximation results for neural networks. Approximation rates for classical function spaces as well…
Approximation Rates for Neural Networks with Encodable Weights in Smoothness Spaces
Ingo Gühring, Mones Raslan
We examine the necessary and sufficient complexity of neural networks to approximate functions from different smoothness spaces under the restriction of encodable network weights.…
Numerical Solution of the Parametric Diffusion Equation by Deep Neural Networks
Moritz Geist, Philipp Petersen, Mones Raslan +2
We perform a comprehensive numerical study of the effect of approximation-theoretical results for neural networks on practical learning problems in the context of numerical analysi…
A Theoretical Analysis of Deep Neural Networks and Parametric PDEs
Gitta Kutyniok, Philipp Petersen, Mones Raslan +1
We derive upper bounds on the complexity of ReLU neural networks approximating the solution maps of parametric partial differential equations. In particular, without any knowledge…