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Landscape analysis for shallow neural networks: complete classification of critical points for affine target functions
Journal of nonlinear science (J. Nonlinear Sci.), 2021
19 March 2021
Patrick Cheridito
Arnulf Jentzen
Florian Rossmannek
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Papers citing
"Landscape analysis for shallow neural networks: complete classification of critical points for affine target functions"
10 / 10 papers shown
A Spin Glass Characterization of Neural Networks
Jun Li
173
1
0
10 Aug 2025
Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escape, and Network Embedding
Zhengqing Wu
Berfin Simsek
Francois Ged
ODL
629
2
0
08 Feb 2024
On the existence of minimizers in shallow residual ReLU neural network optimization landscapes
SIAM Journal on Numerical Analysis (SINUM), 2023
Steffen Dereich
Arnulf Jentzen
Sebastian Kassing
381
9
0
28 Feb 2023
Gradient descent provably escapes saddle points in the training of shallow ReLU networks
Journal of Optimization Theory and Applications (JOTA), 2022
Patrick Cheridito
Arnulf Jentzen
Florian Rossmannek
296
10
0
03 Aug 2022
On the Omnipresence of Spurious Local Minima in Certain Neural Network Training Problems
Constructive approximation (Constr. Approx.), 2022
C. Christof
Julia Kowalczyk
401
10
0
23 Feb 2022
Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions
Martin Hutzenthaler
Arnulf Jentzen
Katharina Pohl
Adrian Riekert
Luca Scarpa
MLT
400
10
0
13 Dec 2021
Existence, uniqueness, and convergence rates for gradient flows in the training of artificial neural networks with ReLU activation
Simon Eberle
Arnulf Jentzen
Adrian Riekert
G. Weiss
237
14
0
18 Aug 2021
A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions
Journal of machine learning research (JMLR), 2021
Arnulf Jentzen
Adrian Riekert
298
19
0
10 Aug 2021
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation
Journal of Mathematical Analysis and Applications (JMAA), 2021
Arnulf Jentzen
Adrian Riekert
259
27
0
09 Jul 2021
A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions
Zeitschrift für Angewandte Mathematik und Physik (ZAMP), 2021
Arnulf Jentzen
Adrian Riekert
MLT
287
15
0
01 Apr 2021
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