Philipp Grohs - RankMe

91 papers A* 6B 2Journal 78Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems. CoRR Michael Scherbela, Nicholas Gao, Philipp Grohs, Stephan Günnemann
2025	J	`jnl`	From completeness of discrete translates to phaseless sampling of the short-time Fourier transform. Adv. Comput. Math. Philipp Grohs, Lukas Liehr, Irina Shafkulovska
2025	J	`jnl`	Phaseless Sampling on Square-Root Lattices. Found. Comput. Math. Philipp Grohs, Lukas Liehr
2025	J	`jnl`	Theory-to-Practice Gap for Neural Networks and Neural Operators. CoRR Philipp Grohs, Samuel Lanthaler, Margaret Trautner
2025	J	`jnl`	Transferable neural wavefunctions for solids. Nat. Comput. Sci. Leon Gerard, Michael Scherbela, Halvard Sutterud, W. Matthew C. Foulkes, Philipp Grohs
2024	J	`jnl`	Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces. Found. Comput. Math. Philipp Grohs, Felix Voigtländer
2024	J	`jnl`	The sampling complexity of learning invertible residual neural networks. CoRR YuanYuan Li, Philipp Grohs, Philipp Petersen
2024	J	`jnl`	Transferable Neural Wavefunctions for Solids. CoRR Leon Gerard, Michael Scherbela, Halvard Sutterud, W. Matthew C. Foulkes, Philipp Grohs
2023	J	`jnl`	FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer. CoRR Pavol Harár, Lukas Herrmann, Philipp Grohs, David Haselbach
2023	A*	`conf`	Learning ReLU networks to high uniform accuracy is intractable. ICLR Julius Berner, Philipp Grohs, Felix Voigtländer
2023	J	`jnl`	Lower bounds for artificial neural network approximations: A proof that shallow neural networks fail to overcome the curse of dimensionality. J. Complex. Philipp Grohs, Shokhrukh Ibragimov, Arnulf Jentzen, Sarah Koppensteiner
2023	J	`jnl`	Non-Uniqueness Theory in Sampled STFT Phase Retrieval. SIAM J. Math. Anal. Philipp Grohs, Lukas Liehr
2023	J	`jnl`	Phase Transitions in Rate Distortion Theory and Deep Learning. Found. Comput. Math. Philipp Grohs, Andreas Klotz, Felix Voigtländer
2023	J	`jnl`	Sampling Complexity of Deep Approximation Spaces. CoRR Ahmed Abdeljawad, Philipp Grohs
2023	J	`jnl`	Space-time error estimates for deep neural network approximations for differential equations. Adv. Comput. Math. Philipp Grohs, Fabian Hornung, Arnulf Jentzen, Philipp Zimmermann
2023	J	`jnl`	Towards a Foundation Model for Neural Network Wavefunctions. CoRR Michael Scherbela, Leon Gerard, Philipp Grohs
2023	A*	`conf`	Variational Monte Carlo on a Budget - Fine-tuning pre-trained Neural Wavefunctions. NeurIPS Michael Scherbela, Leon Gerard, Philipp Grohs
2023	J	`jnl`	Variational Monte Carlo on a Budget - Fine-tuning pre-trained Neural Wavefunctions. CoRR Michael Scherbela, Leon Gerard, Philipp Grohs
2022	A*	`conf`	Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? NeurIPS Leon Gerard, Michael Scherbela, Philipp Marquetand, Philipp Grohs
2022	J	`jnl`	Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? CoRR Leon Gerard, Michael Scherbela, Philipp Marquetand, Philipp Grohs
2022	J	`jnl`	Integral representations of shallow neural network with rectified power unit activation function. Neural Networks Ahmed Abdeljawad, Philipp Grohs
2022	J	`jnl`	Quantification of Kuramoto Coupling Between Intrinsic Brain Networks Applied to fMRI Data in Major Depressive Disorder. Frontiers Comput. Neurosci. Lena G. M. Bauer, Fabian Hirsch, Corey Jones, Matthew Hollander, Philipp Grohs, Amit Anand, Claudia Plant, Afra M. Wohlschläger
2022	J	`jnl`	Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks. Nat. Comput. Sci. Michael Scherbela, Rafael Reisenhofer, Leon Gerard, Philipp Marquetand, Philipp Grohs
2022	J	`jnl`	Training ReLU networks to high uniform accuracy is intractable. CoRR Julius Berner, Philipp Grohs, Felix Voigtländer
2021	J	`jnl`	Approximations with deep neural networks in Sobolev time-space. CoRR Ahmed Abdeljawad, Philipp Grohs
2021	J	`jnl`	Deep Neural Network Approximation Theory. IEEE Trans. Inf. Theory Dennis Elbrächter, Dmytro Perekrestenko, Philipp Grohs, Helmut Bölcskei
2021	J	`jnl`	Deep neural network approximation for high-dimensional parabolic Hamilton-Jacobi-Bellman equations. CoRR Philipp Grohs, Lukas Herrmann
2021	J	`jnl`	Integral representations of shallow neural network with Rectified Power Unit activation function. CoRR Ahmed Abdeljawad, Philipp Grohs
2021	J	`jnl`	Lower bounds for artificial neural network approximations: A proof that shallow neural networks fail to overcome the curse of dimensionality. CoRR Philipp Grohs, Shokhrukh Ibragimov, Arnulf Jentzen, Sarah Koppensteiner
2021	J	`jnl`	Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces. CoRR Philipp Grohs, Felix Voigtländer
2021	J	`jnl`	Sobolev-type embeddings for neural network approximation spaces. CoRR Philipp Grohs, Felix Voigtländer
2021	J	`jnl`	Solving the Kolmogorov PDE by Means of Deep Learning. J. Sci. Comput. Christian Beck, Sebastian Becker, Philipp Grohs, Nor Jaafari, Arnulf Jentzen
2021	J	`jnl`	Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks. CoRR Michael Scherbela, Rafael Reisenhofer, Leon Gerard, Philipp Marquetand, Philipp Grohs
2021	J	`jnl`	Stable Gabor phase retrieval in Gaussian shift-invariant spaces via biorthogonality. CoRR Philipp Grohs, Lukas Liehr
2021	J	`jnl`	The Modern Mathematics of Deep Learning. CoRR Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen
2020	J	`jnl`	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. Julius Berner, Philipp Grohs, Arnulf Jentzen
2020	J	`jnl`	Anisotropic multiscale systems on bounded domains. Adv. Comput. Math. Philipp Grohs, Gitta Kutyniok, Jackie Ma, Philipp Petersen, Mones Raslan
2020	J	`jnl`	Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions. CoRR Philipp Grohs, Lukas Herrmann
2020	A*	`conf`	Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. NeurIPS Julius Berner, Markus Dablander, Philipp Grohs
2020	J	`jnl`	Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. CoRR Julius Berner, Markus Dablander, Philipp Grohs
2020	J	`jnl`	Phase Retrieval: Uniqueness and Stability. SIAM Rev. Philipp Grohs, Sarah Koppensteiner, Martin Rathmair
2020	J	`jnl`	Phase Transitions in Rate Distortion Theory and Deep Learning. CoRR Philipp Grohs, Andreas Klotz, Felix Voigtländer
2019	J	`jnl`	Deep Neural Network Approximation Theory. CoRR Philipp Grohs, Dmytro Perekrestenko, Dennis Elbrächter, Helmut Bölcskei
2019	J	`jnl`	Deep neural network approximations for Monte Carlo algorithms. CoRR Philipp Grohs, Arnulf Jentzen, Diyora Salimova
2019	A*	`conf`	How degenerate is the parametrization of neural networks with the ReLU activation function? NeurIPS Dennis Elbrächter, Julius Berner, Philipp Grohs
2019	J	`jnl`	How degenerate is the parametrization of neural networks with the ReLU activation function? CoRR Julius Berner, Dennis Elbrächter, Philipp Grohs
2019	J	`jnl`	On the approximation of functions with line singularities by ridgelets. J. Approx. Theory Axel Obermeier, Philipp Grohs
2019	J	`jnl`	Optimal Approximation with Sparsely Connected Deep Neural Networks. SIAM J. Math. Data Sci. Helmut Bölcskei, Philipp Grohs, Gitta Kutyniok, Philipp Petersen
2019	—	`conf`	Planting Synchronisation Trees for Discovering Interaction Patterns Among Brain Regions. ICDM Workshops Lena Greta Marie Bauer, Philipp Grohs, Afra M. Wohlschläger, Claudia Plant
2019	J	`jnl`	Projection-Based Finite Elements for Nonlinear Function Spaces. SIAM J. Numer. Anal. Philipp Grohs, Hanne Hardering, Oliver Sander, Markus Sprecher
2019	J	`jnl`	Space-time error estimates for deep neural network approximations for differential equations. CoRR Philipp Grohs, Fabian Hornung, Arnulf Jentzen, Philipp Zimmermann
2019	J	`jnl`	Stable Phase Retrieval in Infinite Dimensions. Found. Comput. Math. Rima Alaifari, Ingrid Daubechies, Philipp Grohs, Rujie Yin
2019	J	`jnl`	The Oracle of DLphi. CoRR 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
2019	J	`jnl`	Towards a regularity theory for ReLU networks - chain rule and global error estimates. CoRR Julius Berner, Dennis Elbrächter, Philipp Grohs, Arnulf Jentzen
2019	J	`jnl`	Uniform error estimates for artificial neural network approximations for heat equations. CoRR Lukas Gonon, Philipp Grohs, Arnulf Jentzen, David Kofler, David Siska
2018	J	`jnl`	A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations. CoRR Philipp Grohs, Fabian Hornung, Arnulf Jentzen, Philippe von Wurstemberger
2018	J	`jnl`	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 Julius Berner, Philipp Grohs, Arnulf Jentzen
2018	J	`jnl`	Energy Propagation in Deep Convolutional Neural Networks. IEEE Trans. Inf. Theory Thomas Wiatowski, Philipp Grohs, Helmut Bölcskei
2018	J	`jnl`	Solving stochastic differential equations and Kolmogorov equations by means of deep learning. CoRR Christian Beck, Sebastian Becker, Philipp Grohs, Nor Jaafari, Arnulf Jentzen
2018	J	`jnl`	The universal approximation power of finite-width deep ReLU networks. CoRR Dmytro Perekrestenko, Philipp Grohs, Dennis Elbrächter, Helmut Bölcskei
2017	J	`jnl`	Energy Propagation in Deep Convolutional Neural Networks. CoRR Thomas Wiatowski, Philipp Grohs, Helmut Bölcskei
2017	B	`conf`	Energy decay and conservation in deep convolutional neural networks. ISIT Philipp Grohs, Thomas Wiatowski, Helmut Bölcskei
2017	J	`jnl`	Optimal Approximation with Sparsely Connected Deep Neural Networks. CoRR Helmut Bölcskei, Philipp Grohs, Gitta Kutyniok, Philipp Petersen
2017	J	`jnl`	Phase Retrieval In The General Setting Of Continuous Frames For Banach Spaces. SIAM J. Math. Anal. Rima Alaifari, Philipp Grohs
2017	J	`jnl`	Scattered manifold-valued data approximation. Numerische Mathematik Philipp Grohs, Markus Sprecher, Thomas Yu
2017	J	`jnl`	Topology Reduction in Deep Convolutional Feature Extraction Networks. CoRR Thomas Wiatowski, Philipp Grohs, Helmut Bölcskei
2016	J	`jnl`	A shearlet-based fast thresholded Landweber algorithm for deconvolution. Int. J. Wavelets Multiresolution Inf. Process. Philipp Grohs, Zeljko Kereta, Uwe Wiesmann
2016	J	`jnl`	Deep Convolutional Neural Networks on Cartoon Functions. CoRR Philipp Grohs, Thomas Wiatowski, Helmut Bölcskei
2016	B	`conf`	Deep convolutional neural networks on cartoon functions. ISIT Philipp Grohs, Thomas Wiatowski, Helmut Bölcskei
2016	A*	`conf`	Discrete Deep Feature Extraction: A Theory and New Architectures. ICML Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Bölcskei
2016	J	`jnl`	Discrete Deep Feature Extraction: A Theory and New Architectures. CoRR Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Bölcskei
2016	J	`jnl`	ε-subgradient algorithms for locally lipschitz functions on Riemannian manifolds. Adv. Comput. Math. Philipp Grohs, Seyedehsomayeh Hosseini
2015	J	`jnl`	FFRT: A Fast Finite Ridgelet Transform for Radiative Transport. Multiscale Model. Simul. Simon Etter, Philipp Grohs, Axel Obermeier
2015	—	`ch.`	From Group Representations to Signal Analysis. Harmonic and Applied Analysis Stephan Dahlke, Filippo De Mari, Philipp Grohs, Demetrio Labate
2015	—	`book`	Harmonic and Applied Analysis - From Groups to Signals Harmonic and Applied Analysis Stephan Dahlke, Filippo De Mari, Philipp Grohs, Demetrio Labate
2015	J	`jnl`	Optimal A Priori Discretization Error Bounds for Geodesic Finite Elements. Found. Comput. Math. Philipp Grohs, Hanne Hardering, Oliver Sander
2015	—	`ch.`	Optimally Sparse Data Representations. Harmonic and Applied Analysis Philipp Grohs
2014	J	`jnl`	Parabolic Molecules. Found. Comput. Math. Philipp Grohs, Gitta Kutyniok
2014	—	`conf`	Ridgelet Methods for Linear Transport Equations. Curves and Surfaces Philipp Grohs, Axel Obermeier
2013	J	`jnl`	Bandlimited shearlet-type frames with nice duals. J. Comput. Appl. Math. Philipp Grohs
2013	J	`jnl`	Geometric multiscale decompositions of dynamic low-rank matrices. Comput. Aided Geom. Des. Philipp Grohs
2013	J	`jnl`	Refinable functions for dilation families. Adv. Comput. Math. Philipp Grohs
2012	J	`jnl`	Definability and stability of multiscale decompositions for manifold-valued data. J. Frankl. Inst. Philipp Grohs, Johannes Wallner
2010	J	`jnl`	A General Proximity Analysis of Nonlinear Subdivision Schemes. SIAM J. Math. Anal. Philipp Grohs
2010	J	`jnl`	Approximation order from stability for nonlinear subdivision schemes. J. Approx. Theory Philipp Grohs
2010	J	`jnl`	Approximation order of interpolatory nonlinear subdivision schemes. J. Comput. Appl. Math. Nira Dyn, Philipp Grohs, Johannes Wallner
2010	J	`jnl`	Edge offset meshes in Laguerre geometry. Adv. Comput. Math. Helmut Pottmann, Philipp Grohs, Bernhard Blaschitz
2009	J	`jnl`	Laguerre minimal surfaces, isotropic geometry and linear elasticity. Adv. Comput. Math. Helmut Pottmann, Philipp Grohs, Niloy J. Mitra
2009	J	`jnl`	Smoothness equivalence properties of univariate subdivision schemes and their projection analogues. Numerische Mathematik Philipp Grohs
2008	J	`jnl`	Smoothness Analysis of Subdivision Schemes on Regular Grids by Proximity. SIAM J. Numer. Anal. Philipp Grohs
2007	J	`jnl`	Smoothness Properties of Lie Group Subdivision Schemes. Multiscale Model. Simul. Johannes Wallner, Esfandiar Nava Yazdani, Philipp Grohs