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Estimation error analysis of deep learning on the regression problem on
  the variable exponent Besov space
v1v2v3v4v5v6 (latest)

Estimation error analysis of deep learning on the regression problem on the variable exponent Besov space

23 September 2020
Kazuma Tsuji
Taiji Suzuki
ArXiv (abs)PDFHTML

Papers citing "Estimation error analysis of deep learning on the regression problem on the variable exponent Besov space"

14 / 14 papers shown
Title
Deep learning from strongly mixing observations: Sparse-penalized
  regularization and minimax optimality
Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax optimality
William Kengne
Modou Wade
66
1
0
12 Jun 2024
How many samples are needed to train a deep neural network?
How many samples are needed to train a deep neural network?
Pegah Golestaneh
Mahsa Taheri
Johannes Lederer
76
4
0
26 May 2024
Robust deep learning from weakly dependent data
Robust deep learning from weakly dependent data
William Kengne
Modou Wade
OOD
82
2
0
08 May 2024
Sup-Norm Convergence of Deep Neural Network Estimator for Nonparametric
  Regression by Adversarial Training
Sup-Norm Convergence of Deep Neural Network Estimator for Nonparametric Regression by Adversarial Training
Masaaki Imaizumi
AAML
68
4
0
08 Jul 2023
Penalized deep neural networks estimator with general loss functions
  under weak dependence
Penalized deep neural networks estimator with general loss functions under weak dependence
William Kengne
Modou Wade
66
2
0
10 May 2023
Pointwise convergence of Fourier series and deep neural network for the
  indicator function of d-dimensional ball
Pointwise convergence of Fourier series and deep neural network for the indicator function of d-dimensional ball
Ryota Kawasumi
Tsuyoshi Yoneda
18
0
0
17 Apr 2023
Pairwise Ranking with Gaussian Kernels
Pairwise Ranking with Gaussian Kernels
Guanhang Lei
Lei Shi
89
2
0
06 Apr 2023
Sparse-penalized deep neural networks estimator under weak dependence
Sparse-penalized deep neural networks estimator under weak dependence
William Kengne
Modou Wade
64
6
0
02 Mar 2023
Confidence-Nets: A Step Towards better Prediction Intervals for
  regression Neural Networks on small datasets
Confidence-Nets: A Step Towards better Prediction Intervals for regression Neural Networks on small datasets
M. Altayeb
A. Elamin
Hozaifa Ahmed
Eithar Elfatih Elfadil Ibrahim
Omer Haydar
Saba Abdulaziz
Najlaa H. M. Mohamed
UQCV
29
0
0
31 Oct 2022
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep
  Learning
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning
Siqi Xu
Lin Liu
Zhong Liu
CMLMedIm
67
9
0
10 Oct 2022
Adaptive deep learning for nonlinear time series models
Adaptive deep learning for nonlinear time series models
Daisuke Kurisu
Riku Fukami
Yuta Koike
AI4TS
58
6
0
06 Jul 2022
On the inability of Gaussian process regression to optimally learn
  compositional functions
On the inability of Gaussian process regression to optimally learn compositional functions
M. Giordano
Kolyan Ray
Johannes Schmidt-Hieber
116
13
0
16 May 2022
Drift estimation for a multi-dimensional diffusion process using deep
  neural networks
Drift estimation for a multi-dimensional diffusion process using deep neural networks
Akihiro Oga
Yuta Koike
DiffM
54
6
0
26 Dec 2021
Nonconvex sparse regularization for deep neural networks and its
  optimality
Nonconvex sparse regularization for deep neural networks and its optimality
Ilsang Ohn
Yongdai Kim
61
19
0
26 Mar 2020
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