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Doing the impossible: Why neural networks can be trained at all
v1v2 (latest)

Doing the impossible: Why neural networks can be trained at all

Nathan Oken Hodas
P. Stinis
ArXiv (abs)PDFHTML

Papers citing "Doing the impossible: Why neural networks can be trained at all"

4 / 4 papers shown
Title
Information-Theoretic Bias Assessment Of Learned Representations Of Pretrained Face Recognition
Information-Theoretic Bias Assessment Of Learned Representations Of Pretrained Face Recognition
Jiazhi Li
Wael AbdAlmageed
155
7
0
08 Nov 2021
Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)
Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)
D. Hopp
AI4TS
63
35
0
15 Jun 2021
PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs
PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEsComputer Methods in Applied Mechanics and Engineering (CMAME), 2019
Xuhui Meng
Zhen Li
Dongkun Zhang
George Karniadakis
PINNAI4CE
166
496
0
23 Sep 2019
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks
P. Stinis
Tobias J. Hagge
A. Tartakovsky
Enoch Yeung
GANAI4CE
128
33
0
22 Mar 2018

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