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ORCIDPhilipp Grohs
Person information
- affiliation: University of Vienna, Austria
- affiliation: ETH Zürich, Switzerland
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2020 – today
- 2024
[j37]Philipp Grohs
, Felix Voigtländer:
Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces. Found. Comput. Math. 24(4): 1085-1143 (2024)- [i35]Leon Gerard, Michael Scherbela, Halvard Sutterud, W. Matthew C. Foulkes, Philipp Grohs:
Transferable Neural Wavefunctions for Solids. CoRR abs/2405.07599 (2024) - 2023
- [j36]Philipp Grohs, Fabian Hornung, Arnulf Jentzen, Philipp Zimmermann:
Space-time error estimates for deep neural network approximations for differential equations. Adv. Comput. Math. 49(1): 4 (2023) - [j35]Philipp Grohs, Andreas Klotz, Felix Voigtländer:
Phase Transitions in Rate Distortion Theory and Deep Learning. Found. Comput. Math. 23(1): 329-392 (2023) - [j34]Philipp Grohs, Shokhrukh Ibragimov, Arnulf Jentzen, Sarah Koppensteiner:
Lower bounds for artificial neural network approximations: A proof that shallow neural networks fail to overcome the curse of dimensionality. J. Complex. 77: 101746 (2023) - [j33]Philipp Grohs, Lukas Liehr:
Non-Uniqueness Theory in Sampled STFT Phase Retrieval. SIAM J. Math. Anal. 55(5): 4695-4726 (2023) - [c10]Julius Berner, Philipp Grohs, Felix Voigtländer:
Learning ReLU networks to high uniform accuracy is intractable. ICLR 2023 - [c9]Michael Scherbela, Leon Gerard, Philipp Grohs:
Variational Monte Carlo on a Budget - Fine-tuning pre-trained Neural Wavefunctions. NeurIPS 2023 - [i34]Michael Scherbela, Leon Gerard, Philipp Grohs:
Towards a Foundation Model for Neural Network Wavefunctions. CoRR abs/2303.09949 (2023) - [i33]Pavol Harár, Lukas Herrmann, Philipp Grohs, David Haselbach:
FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer. CoRR abs/2304.02011 (2023) - [i32]Michael Scherbela, Leon Gerard, Philipp Grohs:
Variational Monte Carlo on a Budget - Fine-tuning pre-trained Neural Wavefunctions. CoRR abs/2307.09337 (2023) - [i31]Ahmed Abdeljawad, Philipp Grohs:
Sampling Complexity of Deep Approximation Spaces. CoRR abs/2312.13379 (2023) - 2022
- [j32]Lena G. M. Bauer, Fabian Hirsch, Corey Jones, Matthew Hollander, Philipp Grohs, Amit Anand, Claudia Plant, Afra M. Wohlschläger:
Quantification of Kuramoto Coupling Between Intrinsic Brain Networks Applied to fMRI Data in Major Depressive Disorder. Frontiers Comput. Neurosci. 16: 729556 (2022) - [j31]Michael Scherbela, Rafael Reisenhofer, Leon Gerard, Philipp Marquetand, Philipp Grohs:
Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks. Nat. Comput. Sci. 2(5): 331-341 (2022) - [j30]Ahmed Abdeljawad, Philipp Grohs:
Integral representations of shallow neural network with rectified power unit activation function. Neural Networks 155: 536-550 (2022) - [c8]Leon Gerard, Michael Scherbela, Philipp Marquetand, Philipp Grohs:
Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? NeurIPS 2022 - [i30]Leon Gerard, Michael Scherbela, Philipp Marquetand, Philipp Grohs:
Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? CoRR abs/2205.09438 (2022) - [i29]Julius Berner, Philipp Grohs, Felix Voigtländer:
Training ReLU networks to high uniform accuracy is intractable. CoRR abs/2205.13531 (2022) - 2021
- [j29]Christian Beck, Sebastian Becker, Philipp Grohs, Nor Jaafari, Arnulf Jentzen:
Solving the Kolmogorov PDE by Means of Deep Learning. J. Sci. Comput. 88(3): 73 (2021) - [j28]Dennis Elbrächter, Dmytro Perekrestenko, Philipp Grohs, Helmut Bölcskei:
Deep Neural Network Approximation Theory. IEEE Trans. Inf. Theory 67(5): 2581-2623 (2021) - [i28]Ahmed Abdeljawad, Philipp Grohs:
Approximations with deep neural networks in Sobolev time-space. CoRR abs/2101.06115 (2021) - [i27]Philipp Grohs, Shokhrukh Ibragimov, Arnulf Jentzen, Sarah Koppensteiner:
Lower bounds for artificial neural network approximations: A proof that shallow neural networks fail to overcome the curse of dimensionality. CoRR abs/2103.04488 (2021) - [i26]Philipp Grohs, Lukas Herrmann:
Deep neural network approximation for high-dimensional parabolic Hamilton-Jacobi-Bellman equations. CoRR abs/2103.05744 (2021) - [i25]Philipp Grohs, Felix Voigtländer:
Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces. CoRR abs/2104.02746 (2021) - [i24]Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen:
The Modern Mathematics of Deep Learning. CoRR abs/2105.04026 (2021) - [i23]Michael Scherbela, Rafael Reisenhofer, Leon Gerard, Philipp Marquetand, Philipp Grohs:
Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks. CoRR abs/2105.08351 (2021) - [i22]Philipp Grohs, Lukas Liehr:
Stable Gabor phase retrieval in Gaussian shift-invariant spaces via biorthogonality. CoRR abs/2109.02494 (2021) - [i21]Philipp Grohs, Felix Voigtländer:
Sobolev-type embeddings for neural network approximation spaces. CoRR abs/2110.15304 (2021) - [i20]Ahmed Abdeljawad, Philipp Grohs:
Integral representations of shallow neural network with Rectified Power Unit activation function. CoRR abs/2112.11157 (2021) - 2020
- [j27]Philipp Grohs, Gitta Kutyniok, Jackie Ma, Philipp Petersen, Mones Raslan:
Anisotropic multiscale systems on bounded domains. Adv. Comput. Math. 46(2): 39 (2020) - [j26]Philipp Grohs, Sarah Koppensteiner, Martin Rathmair:
Phase Retrieval: Uniqueness and Stability. SIAM Rev. 62(2): 301-350 (2020) - [j25]Julius Berner, Philipp Grohs, Arnulf Jentzen:
Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black-Scholes Partial Differential Equations. SIAM J. Math. Data Sci. 2(3): 631-657 (2020) - [c7]Julius Berner, Markus Dablander, Philipp Grohs:
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. NeurIPS 2020 - [i19]Philipp Grohs, Lukas Herrmann:
Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions. CoRR abs/2007.05384 (2020) - [i18]Philipp Grohs, Andreas Klotz, Felix Voigtländer:
Phase Transitions in Rate Distortion Theory and Deep Learning. CoRR abs/2008.01011 (2020) - [i17]Julius Berner, Markus Dablander, Philipp Grohs:
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. CoRR abs/2011.04602 (2020)
2010 – 2019
- 2019
- [j24]Rima Alaifari, Ingrid Daubechies, Philipp Grohs, Rujie Yin:
Stable Phase Retrieval in Infinite Dimensions. Found. Comput. Math. 19(4): 869-900 (2019) - [j23]Axel Obermeier, Philipp Grohs:
On the approximation of functions with line singularities by ridgelets. J. Approx. Theory 237: 30-95 (2019) - [j22]Philipp Grohs, Hanne Hardering, Oliver Sander, Markus Sprecher:
Projection-Based Finite Elements for Nonlinear Function Spaces. SIAM J. Numer. Anal. 57(1): 404-428 (2019) - [j21]Helmut Bölcskei, Philipp Grohs, Gitta Kutyniok, Philipp Petersen:
Optimal Approximation with Sparsely Connected Deep Neural Networks. SIAM J. Math. Data Sci. 1(1): 8-45 (2019) - [c6]Lena Greta Marie Bauer, Philipp Grohs, Afra M. Wohlschläger, Claudia Plant:
Planting Synchronisation Trees for Discovering Interaction Patterns Among Brain Regions. ICDM Workshops 2019: 1035-1036 - [c5]Dennis Elbrächter, Julius Berner, Philipp Grohs:
How degenerate is the parametrization of neural networks with the ReLU activation function? NeurIPS 2019: 7788-7799 - [i16]Philipp Grohs, Dmytro Perekrestenko, Dennis Elbrächter, Helmut Bölcskei:
Deep Neural Network Approximation Theory. CoRR abs/1901.02220 (2019) - [i15]Dominik Alfke, Weston Baines, Jan Blechschmidt, Mauricio J. del Razo Sarmina, Amnon Drory, Dennis Elbrächter, Nando Farchmin, Matteo Gambara, Silke Glas, Philipp Grohs, Peter Hinz, Danijel Kivaranovic, Christian Kümmerle, Gitta Kutyniok, Sebastian Lunz, Jan MacDonald, Ryan Malthaner, Gregory Naisat, Ariel Neufeld, Philipp Christian Petersen, Rafael Reisenhofer, Jun-Da Sheng, Laura Thesing, Philipp Trunschke, Johannes von Lindheim, David Weber, Melanie Weber:
The Oracle of DLphi. CoRR abs/1901.05744 (2019) - [i14]Julius Berner, Dennis Elbrächter, Philipp Grohs, Arnulf Jentzen:
Towards a regularity theory for ReLU networks - chain rule and global error estimates. CoRR abs/1905.04992 (2019) - [i13]Julius Berner, Dennis Elbrächter, Philipp Grohs:
How degenerate is the parametrization of neural networks with the ReLU activation function? CoRR abs/1905.09803 (2019) - [i12]Philipp Grohs, Fabian Hornung, Arnulf Jentzen, Philipp Zimmermann:
Space-time error estimates for deep neural network approximations for differential equations. CoRR abs/1908.03833 (2019) - [i11]Philipp Grohs, Arnulf Jentzen, Diyora Salimova:
Deep neural network approximations for Monte Carlo algorithms. CoRR abs/1908.10828 (2019) - [i10]Lukas Gonon, Philipp Grohs, Arnulf Jentzen, David Kofler, David Siska:
Uniform error estimates for artificial neural network approximations for heat equations. CoRR abs/1911.09647 (2019) - 2018
- [j20]Thomas Wiatowski, Philipp Grohs, Helmut Bölcskei:
Energy Propagation in Deep Convolutional Neural Networks. IEEE Trans. Inf. Theory 64(7): 4819-4842 (2018) - [i9]Christian Beck, Sebastian Becker, Philipp Grohs, Nor Jaafari, Arnulf Jentzen:
Solving stochastic differential equations and Kolmogorov equations by means of deep learning. CoRR abs/1806.00421 (2018) - [i8]Dmytro Perekrestenko, Philipp Grohs, Dennis Elbrächter, Helmut Bölcskei:
The universal approximation power of finite-width deep ReLU networks. CoRR abs/1806.01528 (2018) - [i7]Philipp Grohs, Fabian Hornung, Arnulf Jentzen, Philippe von Wurstemberger:
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations. CoRR abs/1809.02362 (2018) - [i6]Julius Berner, Philipp Grohs, Arnulf Jentzen:
Analysis of the generalization error: Empirical risk minimization over deep artificial neural networks overcomes the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations. CoRR abs/1809.03062 (2018) - 2017
- [j19]Philipp Grohs, Markus Sprecher, Thomas Yu:
Scattered manifold-valued data approximation. Numerische Mathematik 135(4): 987-1010 (2017) - [j18]Rima Alaifari, Philipp Grohs:
Phase Retrieval In The General Setting Of Continuous Frames For Banach Spaces. SIAM J. Math. Anal. 49(3): 1895-1911 (2017) - [c4]Philipp Grohs, Thomas Wiatowski, Helmut Bölcskei:
Energy decay and conservation in deep convolutional neural networks. ISIT 2017: 1356-1360 - [i5]Thomas Wiatowski, Philipp Grohs, Helmut Bölcskei:
Energy Propagation in Deep Convolutional Neural Networks. CoRR abs/1704.03636 (2017) - [i4]Helmut Bölcskei, Philipp Grohs, Gitta Kutyniok, Philipp Petersen:
Optimal Approximation with Sparsely Connected Deep Neural Networks. CoRR abs/1705.01714 (2017) - [i3]Thomas Wiatowski, Philipp Grohs, Helmut Bölcskei:
Topology Reduction in Deep Convolutional Feature Extraction Networks. CoRR abs/1707.02711 (2017) - 2016
- [j17]Philipp Grohs, Seyedehsomayeh Hosseini:
ε-subgradient algorithms for locally lipschitz functions on Riemannian manifolds. Adv. Comput. Math. 42(2): 333-360 (2016) - [j16]Philipp Grohs, Zeljko Kereta, Uwe Wiesmann:
A shearlet-based fast thresholded Landweber algorithm for deconvolution. Int. J. Wavelets Multiresolution Inf. Process. 14(5): 1650032:1-1650032:19 (2016) - [c3]Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Bölcskei:
Discrete Deep Feature Extraction: A Theory and New Architectures. ICML 2016: 2149-2158 - [c2]Philipp Grohs, Thomas Wiatowski, Helmut Bölcskei:
Deep convolutional neural networks on cartoon functions. ISIT 2016: 1163-1167 - [i2]Philipp Grohs, Thomas Wiatowski, Helmut Bölcskei:
Deep Convolutional Neural Networks on Cartoon Functions. CoRR abs/1605.00031 (2016) - [i1]Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Bölcskei:
Discrete Deep Feature Extraction: A Theory and New Architectures. CoRR abs/1605.08283 (2016) - 2015
- [j15]Philipp Grohs, Hanne Hardering, Oliver Sander:
Optimal A Priori Discretization Error Bounds for Geodesic Finite Elements. Found. Comput. Math. 15(6): 1357-1411 (2015) - [j14]Simon Etter, Philipp Grohs, Axel Obermeier:
FFRT: A Fast Finite Ridgelet Transform for Radiative Transport. Multiscale Model. Simul. 13(1): 1-42 (2015) - [p2]Stephan Dahlke, Filippo De Mari, Philipp Grohs, Demetrio Labate:
From Group Representations to Signal Analysis. Harmonic and Applied Analysis 2015: 1-5 - [p1]Philipp Grohs:
Optimally Sparse Data Representations. Harmonic and Applied Analysis 2015: 199-248 - [e1]Stephan Dahlke, Filippo De Mari, Philipp Grohs, Demetrio Labate:
Harmonic and Applied Analysis - From Groups to Signals. Applied and Numerical Harmonic Analysis, Birkhäuser 2015, ISBN 978-3-319-18862-1 [contents] - 2014
- [j13]Philipp Grohs, Gitta Kutyniok:
Parabolic Molecules. Found. Comput. Math. 14(2): 299-337 (2014) - [c1]Philipp Grohs, Axel Obermeier:
Ridgelet Methods for Linear Transport Equations. Curves and Surfaces 2014: 243-262 - 2013
- [j12]Philipp Grohs:
Refinable functions for dilation families. Adv. Comput. Math. 38(3): 531-561 (2013) - [j11]Philipp Grohs:
Geometric multiscale decompositions of dynamic low-rank matrices. Comput. Aided Geom. Des. 30(8): 805-826 (2013) - [j10]Philipp Grohs:
Bandlimited shearlet-type frames with nice duals. J. Comput. Appl. Math. 243: 139-151 (2013) - 2012
- [j9]Philipp Grohs, Johannes Wallner:
Definability and stability of multiscale decompositions for manifold-valued data. J. Frankl. Inst. 349(5): 1648-1664 (2012) - 2010
- [j8]Helmut Pottmann, Philipp Grohs, Bernhard Blaschitz:
Edge offset meshes in Laguerre geometry. Adv. Comput. Math. 33(1): 45-73 (2010) - [j7]Philipp Grohs:
Approximation order from stability for nonlinear subdivision schemes. J. Approx. Theory 162(5): 1085-1094 (2010) - [j6]Nira Dyn, Philipp Grohs, Johannes Wallner:
Approximation order of interpolatory nonlinear subdivision schemes. J. Comput. Appl. Math. 233(7): 1697-1703 (2010) - [j5]Philipp Grohs:
A General Proximity Analysis of Nonlinear Subdivision Schemes. SIAM J. Math. Anal. 42(2): 729-750 (2010)
2000 – 2009
- 2009
- [j4]Helmut Pottmann, Philipp Grohs, Niloy J. Mitra:
Laguerre minimal surfaces, isotropic geometry and linear elasticity. Adv. Comput. Math. 31(4): 391-419 (2009) - [j3]Philipp Grohs:
Smoothness equivalence properties of univariate subdivision schemes and their projection analogues. Numerische Mathematik 113(2): 163-180 (2009) - 2008
- [j2]Philipp Grohs:
Smoothness Analysis of Subdivision Schemes on Regular Grids by Proximity. SIAM J. Numer. Anal. 46(4): 2169-2182 (2008) - 2007
- [j1]Johannes Wallner, Esfandiar Nava Yazdani, Philipp Grohs:
Smoothness Properties of Lie Group Subdivision Schemes. Multiscale Model. Simul. 6(2): 493-505 (2007)
Coauthor Index
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last updated on 2024-10-07 21:24 CEST by the dblp team
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