default search action
Philipp Grohs
Person information
- affiliation: University of Vienna, Austria
- affiliation: ETH Zürich, Switzerland
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
Journal Articles
- 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) - 2023
- [j36]Philipp Grohs, Fabian Hornung, Arnulf Jentzen
Space-time error estimates for deep neural network approximations for differential equations. Adv. Comput. Math. 49(1): 4 (2023) - [j35]Philipp Grohs, Andreas Klotz
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) - 2022
- [j32]Lena G. M. Bauer, Fabian Hirsch
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
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
Integral representations of shallow neural network with rectified power unit activation function. Neural Networks 155: 536-550 (2022) - 2021
- [j29]Christian Beck, Sebastian Becker, Philipp Grohs, Nor Jaafari
Solving the Kolmogorov PDE by Means of Deep Learning. J. Sci. Comput. 88(3): 73 (2021) - [j28]Dennis Elbrächter, Dmytro Perekrestenko
Deep Neural Network Approximation Theory. IEEE Trans. Inf. Theory 67(5): 2581-2623 (2021) - 2020
- [j27]Philipp Grohs, Gitta Kutyniok
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
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) - 2019
- [j24]Rima Alaifari
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
Projection-Based Finite Elements for Nonlinear Function Spaces. SIAM J. Numer. Anal. 57(1): 404-428 (2019) - [j21]Helmut Bölcskei
Optimal Approximation with Sparsely Connected Deep Neural Networks. SIAM J. Math. Data Sci. 1(1): 8-45 (2019) - 2018
- [j20]Thomas Wiatowski
Energy Propagation in Deep Convolutional Neural Networks. IEEE Trans. Inf. Theory 64(7): 4819-4842 (2018) - 2017
- [j19]Philipp Grohs
Scattered manifold-valued data approximation. Numerische Mathematik 135(4): 987-1010 (2017) - [j18]Rima Alaifari
Phase Retrieval In The General Setting Of Continuous Frames For Banach Spaces. SIAM J. Math. Anal. 49(3): 1895-1911 (2017) - 2016
- [j17]Philipp Grohs
ε-subgradient algorithms for locally lipschitz functions on Riemannian manifolds. Adv. Comput. Math. 42(2): 333-360 (2016) - [j16]Philipp Grohs
A shearlet-based fast thresholded Landweber algorithm for deconvolution. Int. J. Wavelets Multiresolution Inf. Process. 14(5): 1650032:1-1650032:19 (2016) - 2015
- [j15]Philipp Grohs, Hanne Hardering
Optimal A Priori Discretization Error Bounds for Geodesic Finite Elements. Found. Comput. Math. 15(6): 1357-1411 (2015) - [j14]Simon Etter
FFRT: A Fast Finite Ridgelet Transform for Radiative Transport. Multiscale Model. Simul. 13(1): 1-42 (2015) - 2014
- [j13]Philipp Grohs, Gitta Kutyniok
Parabolic Molecules. Found. Comput. Math. 14(2): 299-337 (2014) - 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
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) - 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
Smoothness Properties of Lie Group Subdivision Schemes. Multiscale Model. Simul. 6(2): 493-505 (2007)
Conference and Workshop Papers
- 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 - 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 - 2020
- [c7]Julius Berner, Markus Dablander, Philipp Grohs:
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. NeurIPS 2020 - 2019
- [c6]Lena Greta Marie Bauer, Philipp Grohs
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 - 2017
- [c4]Philipp Grohs
Energy decay and conservation in deep convolutional neural networks. ISIT 2017: 1356-1360 - 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 - 2014
- [c1]Philipp Grohs, Axel Obermeier:
Ridgelet Methods for Linear Transport Equations. Curves and Surfaces 2014: 243-262
Parts in Books or Collections
- 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
Editorship
- 2015
- [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]
Informal and Other Publications
- 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
- [i34]Michael Scherbela, Leon Gerard, Philipp Grohs:
Towards a Foundation Model for Neural Network Wavefunctions. CoRR abs/2303.09949 (2023) - [i33]Pavol Harár
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
- [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
- [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
The Modern Mathematics of Deep Learning. CoRR abs/2105.04026 (2021) - [i23]Michael Scherbela
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
- [i19]Philipp Grohs
Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions. CoRR abs/2007.05384 (2020) - [i18]Philipp Grohs
Phase Transitions in Rate Distortion Theory and Deep Learning. CoRR abs/2008.01011 (2020) - [i17]Julius Berner, Markus Dablander
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. CoRR abs/2011.04602 (2020) - 2019
- [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
- [i9]Christian Beck
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
- [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
- [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)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-07 21:24 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: