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Philipp Grohs

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with Julius Berner

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Publications

2023
[c10]
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  dblp key:
  - conf/iclr/BernerGV23
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  - https://dblp.org/rec/conf/iclr/BernerGV23
Julius Berner, Philipp Grohs, Felix Voigtländer:
Learning ReLU networks to high uniform accuracy is intractable. ICLR 2023
2022
[i29]
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  - journals/corr/abs-2205-13531
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  - https://dblp.org/rec/journals/corr/abs-2205-13531
Julius Berner, Philipp Grohs, Felix Voigtländer:
Training ReLU networks to high uniform accuracy is intractable. CoRR abs/2205.13531 (2022)
2021
[i24]
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  - journals/corr/abs-2105-04026
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Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen:
The Modern Mathematics of Deep Learning. CoRR abs/2105.04026 (2021)
2020
[j25]
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  - journals/simods/BernerGJ20
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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]
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  - electronic edition @ neurips.cc (open access)
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  - conf/nips/BernerDG20
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Julius Berner, Markus Dablander, Philipp Grohs:
Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning. NeurIPS 2020
[i17]
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  - journals/corr/abs-2011-04602
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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)
2019
[c5]
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  - conf/nips/ElbrachterBG19
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Dennis Elbrächter, Julius Berner, Philipp Grohs:
How degenerate is the parametrization of neural networks with the ReLU activation function? NeurIPS 2019: 7788-7799
[i14]
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  dblp key:
  - journals/corr/abs-1905-04992
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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]
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  - journals/corr/abs-1905-09803
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Julius Berner, Dennis Elbrächter, Philipp Grohs:
How degenerate is the parametrization of neural networks with the ReLU activation function? CoRR abs/1905.09803 (2019)
2018
[i6]
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  dblp key:
  - journals/corr/abs-1809-03062
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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)

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