Showing entries 1 - 20 out of 102
Harar P, Herrmann L, Grohs P, Haselbach D. FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer. 2023. Epub 2023 Apr 17. doi: 10.48550/ARXIV.2304.02011

Grohs P, Voigtländer F. Sobolev-type embeddings for neural network approximation spaces. 2 ed. 2023 Apr, p. 579-599. doi: 10.1007/s00365-022-09598-x

Liehr L, Grohs P. Non-uniqueness theory in sampled STFT phase retrieval. SIAM Journal on Mathematical Analysis. 2023 Mar 21. Epub 2023 Mar 21. doi:

Grohs P, Klotz A, Voigtlaender F. Phase Transitions in Rate Distortion Theory and Deep Learning. Foundations of Computational Mathematics. 2023 Feb;23(1):329-392. Epub 2021 Nov 16. doi:

Grohs P, Liehr L. Injectivity of Gabor phase retrieval from lattice measurements. Applied and Computational Harmonic Analysis. 2023 Jan;62:173-193. doi:, 10.1016/j.acha.2022.09.001

Grohs P, Hornung F, Jentzen A, Zimmermann P. Space-time error estimates for deep neural network approximations for differential equations. Advances in Computational Mathematics. 2023;49:4. doi: 10.48550/arXiv.1908.03833,

Abdeljawad A, Grohs P. Integral representations of shallow neural network with Rectified Power Unit activation function. Neural Networks. 2022 Nov;155:536 - 550. Epub 2022 Sep 14. doi: 10.1016/j.neunet.2022.09.005

Liehr L, Grohs P. Phaseless sampling on square-root lattices. 2022 Sep 22. doi: 10.48550/arXiv.2209.11127

Grohs P, Herrmann L. Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions. IMA Journal of Numerical Analysis. 2022 Jul 22;42(3):2055-2082. drab031. Epub 2021 May 10. doi: 10.1093/imanum/drab031

Liehr L, Grohs P, Rathmair M. Multi-window STFT phase retrieval: lattice uniqueness. 2022 Jul 21. doi: 10.48550/arXiv.2207.10620

Gonon L, Grohs P, Jentzen A, Kofler D, Siska D. Uniform error estimates for artificial neural network approximations for heat equations. IMA Journal of Numerical Analysis. 2022 Jul;42(3):1991-2054. drab027. Epub 2021 Aug 10. doi: 10.1093/imanum/drab027

Abdeljawad A, Grohs P. Approximations with deep neural networks in Sobolev time-space. Analysis and Applications. 2022 May 10;20(3):499 -541. doi:

Schneckenreiter G, Herrmann L, Reisenhofer R, Popper N, Grohs P. Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data. medRxiv. 2022 Mar 1. doi:

Elbrächter D, Grohs P, Jentzen A, Schwab C. DNN Expression Rate Analysis of High-dimensional PDEs: Application to Option Pricing. Constructive Approximation. 2022 Feb;55(1):3-71. Epub 2021 May 6. doi: 10.1007/s00365-021-09541-6

Berner J, Grohs P, Kutyniok G, Petersen PC. Die Moderne Mathematik des Tiefen Lernens. In Mitteilungen der DMV. 4 ed. Vol. 29. De Gruyter. 2022. p. 191-197. (Mitteilungen der Deutschen Mathematiker-Vereinigung). doi:

Showing entries 1 - 20 out of 102