Publications
Stable Gabor Phase Retrieval in Gaussian Shift-Invariant Spaces via Biorthogonality. / Liehr, Lukas; Grohs, Philipp.
In: Constructive Approximation, Vol. 59, No. 1, 02.2024, p. 61-111.Publications: Contribution to journal › Article › peer-review
Towards a transferable fermionic neural wavefunction for molecules. / Scherbela, Michael; Gerard, Leon; Grohs, Philipp.
In: Nature Communications, Vol. 15, No. 1, 120, 2024.Publications: Contribution to journal › Article › peer-review
FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer. / Harar, Pavol; Herrmann, Lukas; Grohs, Philipp et al.
2023.Publications: Other publication › Other
Sobolev-type embeddings for neural network approximation spaces. / Grohs, Philipp; Voigtländer, Felix.
2. ed. 2023. p. 579-599.Publications: Working paper › Preprint
Non-uniqueness theory in sampled STFT phase retrieval. / Liehr, Lukas; Grohs, Philipp.
In: SIAM Journal on Mathematical Analysis, 21.03.2023.Publications: Contribution to journal › Article › peer-review
Phaseless sampling on square-root lattices. / Liehr, Lukas; Grohs, Philipp.
In: Foundations of Computational Mathematics, 08.02.2023.Publications: Contribution to journal › Article › peer-review
Phase Transitions in Rate Distortion Theory and Deep Learning. / Grohs, Philipp; Klotz, Andreas; Voigtlaender, Felix (Corresponding author).
In: Foundations of Computational Mathematics, Vol. 23, No. 1, 02.2023, p. 329-392.Publications: Contribution to journal › Article › peer-review
Injectivity of Gabor phase retrieval from lattice measurements. / Grohs, Philipp; Liehr, Lukas (Corresponding author).
In: Applied and Computational Harmonic Analysis, Vol. 62, 01.2023, p. 173-193.Publications: Contribution to journal › Article › peer-review
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations. / Grohs, Philipp; Hornung, Fabian ; Jentzen, Arnulf et al.
In: Memoirs of the American Mathematical Society, Vol. 284, No. 1410, 2023.Publications: Contribution to journal › Article › peer-review
Arithmetic progressions and holomorphic phase retrieval. / Liehr, Lukas.
2023.Publications: Working paper › Preprint
Learning ReLU networks to high uniform accuracy is intractable. / Berner, Julius; Grohs, Philipp; Voigtlaender, Felix.
2023. Paper presented at The Eleventh International Conference on Learning Representations: ICLR 2023.Publications: Contribution to conference › Paper › peer-review
Phase retrieval in Fock space and perturbation of Liouville sets. / Liehr, Lukas; Grohs, Philipp; Rathmair, Martin.
2023.Publications: Working paper › Preprint
Space-time error estimates for deep neural network approximations for differential equations. / Grohs, Philipp; Hornung, Fabian ; Jentzen, Arnulf et al.
In: Advances in Computational Mathematics, Vol. 49, 4, 2023.Publications: Contribution to journal › Article › peer-review
Variational Monte Carlo on a Budget - Fine-tuning pretrained Neural Wavefunctions. / Scherbela, Michael; Gerard, Leon; Grohs, Philipp.
Advances in Neural Information Processing Systems 36 (NeurIPS 2023). 2023.Publications: Contribution to book › Contribution to proceedings › peer-review
From completeness of discrete translates to phaseless sampling of the short-time Fourier transform. / Liehr, Lukas; Grohs, Philipp; Shafkulovska, Irina.
2022.Publications: Working paper › Preprint
Exploring Language-independent Digital Speech Biomarkers of Hypokinetic Dysarthria. / Kovac, Daniel; Mekyska, Jiri; Aharonson, Vered et al.
medRxiv, 2022.Publications: Working paper › Preprint
Integral representations of shallow neural network with Rectified Power Unit activation function. / Abdeljawad, Ahmed (Corresponding author); Grohs, Philipp.
In: Neural Networks, Vol. 155, 11.2022, p. 536 - 550.Publications: Contribution to journal › Article › peer-review
Redistributor: Transforming Empirical Data Distributions. / Harar, Pavol; Elbrächter, Dennis; Dörfler, Monika et al.
arXiv, 2022.Publications: Working paper › Preprint
Phaseless sampling on square-root lattices. / Liehr, Lukas; Grohs, Philipp.
2022.Publications: Working paper › Preprint
Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms. / Grohs, Philipp; Jentzen, Arnulf (Corresponding author); Salimova , Diyora .
In: SN Partial Differential Equations and Applications, Vol. 3, No. 4, 45, 08.2022.Publications: Contribution to journal › Article › peer-review