Kontakt
Email: Philipp.Grohs(at)oeaw.ac.at
Telefon: +43 1 4277 50613
Veröffentlichungen
Beitrag in Fachzeitschrift (16)
- Grohs, Philipp; Liehr, Lukas (2023) Stable Gabor Phase Retrieval in Gaussian Shift-Invariant Spaces via Biorthogonality. CONSTR APPROX.DOI RIS ENW BIB
- Grohs, Philipp; Ibragimov, Shokhrukh; Jentzen, Arnulf; Koppensteiner, Sarah (2023) Lower bounds for artificial neural network approximations: A proof that shallow neural networks fail to overcome the curse of dimensionality. J COMPLEXITY, Bd. 77, S. ARTN 101746.DOI RIS ENW BIB
- Grohs, Philipp; Voigtlaender, Felix (2023) Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces. FOUND COMPUT MATH.DOI RIS ENW BIB
- Grohs, Philipp; Liehr, Lukas (2023) NON-UNIQUENESS THEORY IN SAMPLED STFT PHASE RETRIEVAL. SIAM J MATH ANAL, Bd. 55 (5), S. 4695-4726.DOI RIS ENW BIB
- Schneckenreither, Guenter; Herrmann, Lukas; Reisenhofer, Rafael; Popper, Niki; Grohs, Philipp (2023) Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data. PLOS ONE, Bd. 18 (5).DOI RIS ENW BIB
- Grohs, Philipp; Hornung, Fabian; Jentzen, Arnulf; von Wurstemberger, Philippe (2023) A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black-Scholes Partial Differential Equations. MEM AM MATH SOC, Bd. 284 (1410), S. 1-106.DOI RIS ENW BIB
- Grohs, Philipp; Voigtlaender, Felix (2023) Sobolev-Type Embeddings for Neural Network Approximation Spaces. CONSTR APPROX.DOI RIS ENW BIB
- Grohs, Philipp; Liehr, Lukas (2023) Injectivity of Gabor phase retrieval from lattice measurements. APPL COMPUT HARMON A, Bd. 62, S. 173-193.DOI RIS ENW BIB
- Abdeljawad, Ahmed; Grohs, Philipp (2022) Integral representations of shallow neural network with rectified power unit activation function. Neural Netw., Bd. 155, S. 536-550.DOI RIS ENW BIB
- Grohs, Philipp; Rathmair, Martin (2022) Stable Gabor phase retrieval for multivariate functions. J. Eur. Math. Soc., Bd. 24 (5), S. 1593-1615.DOI RIS ENW BIB
- Abdeljawad, Ahmed; Grohs, Philipp (2022) Approximations with deep neural networks in Sobolev time-space. Anal. Appl., Bd. 20 (03), S. 499-541.DOI RIS ENW BIB
- Scherbela, Michael; Reisenhofer, Rafael; Gerard, Leon; Marquetand, Philipp; Grohs, Philipp (2022) Solving the electronic Schrodinger equation for multiple nuclear geometries with weight-sharing deep neural networks. NATURE COMPUTATIONAL SCIENCE, Bd. 2 (5), S. 331-341.DOI RIS ENW BIB
- Grohs, Philipp; Liehr, Lukas (2022) On Foundational Discretization Barriers in STFT Phase Retrieval. J. Fourier Anal. Appl., Bd. 28 (2), S. ARTN 39.DOI RIS ENW BIB
- Grohs, P.; Kritzer, P.; Kunisch, K.; Ramlau, R.; Scherzer, O. (2021) RICAM, the Johann Radon Institute for Computational and Applied Mathematics. European Mathematical Society Magazine, Bd. 122, S. 46-51.RIS ENW BIB
- Verdun, Claudio M.; Fuchs, Tim; Harar, Pavol; Elbrachter, Dennis; Fischer, David S. et al. [..] (2021) Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies. FRONTIERS IN PUBLIC HEALTH, Bd. 9, S. ARTN 583377.DOI RIS ENW BIB
- Elbraechter, Dennis; Grohs, Philipp; Jentzen, Arnulf; Schwab, Christoph (2021) DNN Expression Rate Analysis of High-Dimensional PDEs: Application to Option Pricing. Constr. Approx.DOI RIS ENW BIB