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Convergence rates of deep ReLU networks for multiclass classification

Convergence rates of deep ReLU networks for multiclass classification

2 August 2021
Thijs Bos
Johannes Schmidt-Hieber
ArXiv (abs)PDFHTML

Papers citing "Convergence rates of deep ReLU networks for multiclass classification"

17 / 17 papers shown
Super-fast rates of convergence for Neural Networks Classifiers under the Hard Margin Condition
Super-fast rates of convergence for Neural Networks Classifiers under the Hard Margin Condition
Nathanael Tepakbong
Ding-Xuan Zhou
Xiang Zhou
445
0
0
13 May 2025
Deep learning with missing data
Deep learning with missing data
Tianyi Ma
Tengyao Wang
R. Samworth
513
3
0
21 Apr 2025
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
231
7
0
26 May 2024
Misclassification bounds for PAC-Bayesian sparse deep learning
Misclassification bounds for PAC-Bayesian sparse deep learningMachine-mediated learning (ML), 2024
The Tien Mai
UQCVBDL
313
8
0
02 May 2024
On the rates of convergence for learning with convolutional neural networks
On the rates of convergence for learning with convolutional neural networks
Yunfei Yang
Han Feng
Ding-Xuan Zhou
403
4
0
25 Mar 2024
Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax
  Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes
Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function ClassesInternational Conference on Machine Learning (ICML), 2024
Hyunouk Ko
Xiaoming Huo
222
1
0
08 Jan 2024
Classification of Data Generated by Gaussian Mixture Models Using Deep
  ReLU Networks
Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU NetworksJournal of machine learning research (JMLR), 2023
Tiancong Zhou
X. Huo
161
5
0
15 Aug 2023
Multiclass classification for multidimensional functional data through
  deep neural networks
Multiclass classification for multidimensional functional data through deep neural networksElectronic Journal of Statistics (EJS), 2023
Shuoyang Wang
Guanqun Cao
222
7
0
22 May 2023
Minimax optimal high-dimensional classification using deep neural
  networks
Minimax optimal high-dimensional classification using deep neural networks
Shuoyang Wang
Zuofeng Shang
169
4
0
04 Mar 2023
Orthogonal Series Estimation for the Ratio of Conditional Expectation
  Functions
Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions
Kazuhiko Shinoda
T. Hoshino
CML
177
0
0
26 Dec 2022
Automatic Change-Point Detection in Time Series via Deep Learning
Automatic Change-Point Detection in Time Series via Deep Learning
Jie Li
Paul Fearnhead
Piotr Fryzlewicz
Teng Wang
AI4TS
200
28
0
07 Nov 2022
Optimal Convergence Rates of Deep Neural Networks in a Classification
  Setting
Optimal Convergence Rates of Deep Neural Networks in a Classification SettingElectronic Journal of Statistics (EJS), 2022
Josephine T. Meyer
146
2
0
25 Jul 2022
Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision
  Boundary
Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary
Tianyang Hu
Ruiqi Liu
Zuofeng Shang
Guang Cheng
137
3
0
04 Jul 2022
Deep Neural Network Classifier for Multi-dimensional Functional Data
Deep Neural Network Classifier for Multi-dimensional Functional DataScandinavian Journal of Statistics (Scand. J. Stat.), 2022
Shuoyang Wang
Guanqun Cao
Zuofeng Shang
181
14
0
17 May 2022
Analysis of convolutional neural network image classifiers in a
  rotationally symmetric model
Analysis of convolutional neural network image classifiers in a rotationally symmetric modelIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2022
Michael Kohler
Benjamin Kohler
155
6
0
11 May 2022
Optimal Convergence Rates of Deep Convolutional Neural Networks:
  Additive Ridge Functions
Optimal Convergence Rates of Deep Convolutional Neural Networks: Additive Ridge Functions
Zhiying Fang
Guang Cheng
MLT
191
5
0
24 Feb 2022
Statistical theory for image classification using deep convolutional
  neural networks with cross-entropy loss under the hierarchical max-pooling
  model
Statistical theory for image classification using deep convolutional neural networks with cross-entropy loss under the hierarchical max-pooling modelJournal of Statistical Planning and Inference (JSPI), 2020
Michael Kohler
S. Langer
255
20
0
27 Nov 2020
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