Philipp Grohs
Dr. techn. Technische Universität Wien 2007
Facts
12 properties- Instance of
- human
- Date of birth
- 1981
- Occupation
- mathematician, university teacher
- Employer
- University of Vienna[8]
- Doctoral student
- Axel Clemens Obermeier, Simon Pintarelli, Markus Sprecher, Zeljko Kereta[11][12][13][14]
- Student of
- Johannes Wallner[17]
🏷️ Also known as
en Philipp Grohs
🔖 Identifiers · 13
- ISNI
- 0000000409927769
- ORCID iD
- 0000-0001-9205-0969
- VIAF cluster ID
- 304138773
- GND ID
- 1036788318
- DBLP author ID
- 11/7839
- DDB person (GND) ID
- 1036788318
- Google Knowledge Graph ID
- /g/11fyxp17jj
- J9U entity ID
- 987012384808405171
- MR Author ID
- 821216
- Mathematics Genealogy Project ID
- 114741
- NL CR AUT ID
- ntk20231184199
- NUKAT ID
- n2015068319
Show all 13 identifiers +1
- Yale LUX ID
- person/40764ed6-4b05-45b8-9a76-ca138f6c6036
🔗 Connections
Author of 15
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,
Approximations with deep neural networks in Sobolev time-space,
A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations,
Cartoon Approximation with $$\alpha $$ α -Curvelets,
Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms,
Energy Propagation in Deep Convolutional Neural Networks,
How degenerate is the parametrization of neural networks with the ReLU activation function,
Injectivity of Gabor phase retrieval from lattice measurements
+7 more
← Authored by 15
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,
Approximations with deep neural networks in Sobolev time-space,
A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations,
Cartoon Approximation with $$\alpha $$ α -Curvelets,
Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms,
Energy Propagation in Deep Convolutional Neural Networks,
How degenerate is the parametrization of neural networks with the ReLU activation function,
Injectivity of Gabor phase retrieval from lattice measurements
+7 more
Field of work 5
Affiliated with 4
Occupation 2
Educated at 1
Employer 1
Instance of 1
Sex or gender 1
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