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Quantifying the generalization error in deep learning in terms of data
  distribution and neural network smoothness
v1v2v3 (latest)

Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness

27 May 2019
Pengzhan Jin
Lu Lu
Yifa Tang
George Karniadakis
ArXiv (abs)PDFHTML

Papers citing "Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness"

16 / 16 papers shown
Title
Reliable extrapolation of deep neural operators informed by physics or
  sparse observations
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Min Zhu
Handi Zhang
Anran Jiao
George Karniadakis
Lu Lu
106
100
0
13 Dec 2022
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
Brian Bartoldson
B. Kailkhura
Davis W. Blalock
109
51
0
13 Oct 2022
Certified machine learning: Rigorous a posteriori error bounds for PDE
  defined PINNs
Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs
Birgit Hillebrecht
B. Unger
PINN
84
5
0
07 Oct 2022
Overview frequency principle/spectral bias in deep learning
Overview frequency principle/spectral bias in deep learning
Z. Xu
Yaoyu Zhang
Yaoyu Zhang
FaML
92
74
0
19 Jan 2022
Embedding Principle: a hierarchical structure of loss landscape of deep
  neural networks
Embedding Principle: a hierarchical structure of loss landscape of deep neural networks
Yaoyu Zhang
Yuqing Li
Zhongwang Zhang
Yaoyu Zhang
Z. Xu
86
23
0
30 Nov 2021
Approximation capabilities of measure-preserving neural networks
Approximation capabilities of measure-preserving neural networks
Aiqing Zhu
Pengzhan Jin
Yifa Tang
84
8
0
21 Jun 2021
Embedding Principle of Loss Landscape of Deep Neural Networks
Embedding Principle of Loss Landscape of Deep Neural Networks
Yaoyu Zhang
Zhongwang Zhang
Yaoyu Zhang
Z. Xu
69
38
0
30 May 2021
Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on
  Unseen Domains
Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on Unseen Domains
Hengjie Wang
R. Planas
Aparna Chandramowlishwaran
Ramin Bostanabad
AI4CE
149
64
0
22 Apr 2021
On Theory-training Neural Networks to Infer the Solution of Highly
  Coupled Differential Equations
On Theory-training Neural Networks to Infer the Solution of Highly Coupled Differential Equations
M. T. Rad
A. Viardin
M. Apel
AI4CE
42
3
0
09 Feb 2021
Physics-informed neural networks with hard constraints for inverse
  design
Physics-informed neural networks with hard constraints for inverse design
Lu Lu
R. Pestourie
Wenjie Yao
Zhicheng Wang
F. Verdugo
Steven G. Johnson
PINN
102
522
0
09 Feb 2021
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Juntang Zhuang
Nicha Dvornek
Xiaoxiao Li
S. Tatikonda
X. Papademetris
James Duncan
BDL
127
112
0
03 Jun 2020
SympNets: Intrinsic structure-preserving symplectic networks for
  identifying Hamiltonian systems
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems
Pengzhan Jin
Zhen Zhang
Aiqing Zhu
Yifa Tang
George Karniadakis
105
21
0
11 Jan 2020
Deep learning architectures for nonlinear operator functions and
  nonlinear inverse problems
Deep learning architectures for nonlinear operator functions and nonlinear inverse problems
Maarten V. de Hoop
Matti Lassas
C. Wong
78
26
0
23 Dec 2019
Variational Physics-Informed Neural Networks For Solving Partial
  Differential Equations
Variational Physics-Informed Neural Networks For Solving Partial Differential Equations
E. Kharazmi
Z. Zhang
George Karniadakis
108
246
0
27 Nov 2019
DeepONet: Learning nonlinear operators for identifying differential
  equations based on the universal approximation theorem of operators
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
Lu Lu
Pengzhan Jin
George Karniadakis
254
2,179
0
08 Oct 2019
DeepXDE: A deep learning library for solving differential equations
DeepXDE: A deep learning library for solving differential equations
Lu Lu
Xuhui Meng
Zhiping Mao
George Karniadakis
PINNAI4CE
101
1,555
0
10 Jul 2019
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