Fetching the paper…
Reading the bibliography…
In this paper, we consider the problem of binary classification with a class of general deep convolutional neural networks, which includes fully-connected neural networks and fully convolutional neural networks as special cases.
Nonparametric regression on low-dimensional manifolds using deep relu networks
Minshuo Chen, Haoming Jiang, Wenjing Liao, and Tuo Zhao · 1908
Earlier work this paper cites.
The regression analysis of binary sequences
D. R. Cox · 1958
Earlier work this paper cites.
Theory of pattern recognition, 1974
Vladimir Vapnik and Alexey Chervonenkis · 1974
Earlier work this paper cites.
The estimation of a simultaneous equation generalized probit model
Takeshi Amemiya · 1978
Earlier work this paper cites.
Optimal global rates of convergence for nonparametric regression
Charles J. Stone · 1982
Earlier work this paper cites.
Optimal rates of convergence to bayes risk in nonparametric discrimination
James Stephen Marron et al · 1983
Earlier work this paper cites.
Classification and Regression Trees
L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone · 1984
Earlier work this paper cites.
Learnability and the vapnik-chervonenkis dimension
Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, and Manfred K Warmuth · 1989
Earlier work this paper cites.
A survey of decision tree classifier methodology
S Rasoul Safavian and David Landgrebe · 1991
Earlier work this paper cites.
A training algorithm for optimal margin classifiers
Bernhard E. Boser, Isabelle M. Guyon, and Vladimir N. Vapnik · 1992
Earlier work this paper cites.
Sharper bounds for gaussian and empirical processes
Michel Talagrand · 1994
Earlier work this paper cites.
Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
Earlier work this paper cites.
Experiments with a new boosting algorithm
Yoav Freund, Robert E Schapire, et al · 1996
Earlier work this paper cites.
An Introduction to Support Vector Machines: And Other Kernel-Based Learning Methods
Nello Cristianini and John Shawe-Taylor · 1999
Earlier work this paper cites.
Smooth discrimination analysis
Enno Mammen and Alexandre B. Tsybakov · 1999
Earlier work this paper cites.
Minimax nonparametric classification. i. rates of convergence
Yuhong Yang · 1999
Earlier work this paper cites.
Additive logistic regression: a statistical view of boosting
Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2000
Earlier work this paper cites.
Random forests
Leo Breiman · 2001
Earlier work this paper cites.
The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2001
Earlier work this paper cites.
Logistic regression
David G Kleinbaum, K Dietz, M Gail, Mitchel Klein, and Mitchell Klein · 2002
Earlier work this paper cites.
On ψ \psi -learning
Xiaotong Shen, George C Tseng, Xuegong Zhang, and Wing Hung Wong · 2003
Earlier work this paper cites.
A note on margin-based loss functions in classification
Yi Lin · 2004
Earlier work this paper cites.
On the Bayes-risk consistency of regularized boosting methods
Gábor Lugosi and Nicolas Vayatis · 2004
Earlier work this paper cites.
Optimal aggregation of classifiers in statistical learning
Alexandre B. Tsybakov · 2004
Earlier work this paper cites.
Statistical behavior and consistency of classification methods based on convex risk minimization
Tong Zhang · 2004
Earlier work this paper cites.
Theory of classification: A survey of some recent advances
Stéphane Boucheron, Olivier Bousquet, and Gábor Lugosi · 2005
Earlier work this paper cites.
Empirical minimization
Peter L Bartlett and Shahar Mendelson · 2006
Cited alongside, same era.
Convexity, classification, and risk bounds
Peter L. Bartlett, Michael I. Jordan, and Jon D. McAuliffe · 2006
Cited alongside, same era.
Active learning in the non-realizable case
Matti Kääriäinen · 2006
Cited alongside, same era.
Multicategory ψ \psi -learning
Yufeng Liu and Xiaotong Shen · 2006
Cited alongside, same era.
Risk bounds for statistical learning
Pascal Massart and Élodie Nédélec · 2006
Cited alongside, same era.
Fast learning rates for plug-in classifiers
Jean-Yves Audibert, Alexandre B Tsybakov, et al · 2007
Cited alongside, same era.
Distance-weighted discrimination
J. S. Marron, Michael J. Todd, and Jeongyoun Ahn · 2007
On the expressive power of deep neural networks
Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, and Jascha Sohl-Dickstein · 2017
Later among the works it cites.
Error bounds for approximations with deep relu networks
Dmitry Yarotsky · 2017
Later among the works it cites.
Stronger generalization bounds for deep nets via a compression approach
Sanjeev Arora, Rong Ge, Behnam Neyshabur, and Yi Zhang · 2018
Later among the works it cites.
On tighter generalization bound for deep neural networks: Cnns, resnets, and beyond
Xingguo Li, Junwei Lu, Zhaoran Wang, Jarvis Haupt, and Tuo Zhao · 2018
Later among the works it cites.
Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
Later among the works it cites.
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Agnostically learning halfspaces
Adam Tauman Kalai, Adam R. Klivans, Yishay Mansour, and Rocco A. Servedio · 2008
Cited alongside, same era.
On the efficient minimization of classification calibrated surrogates
Richard Nock and Frank Nielsen · 2008
Cited alongside, same era.
Random projections of smooth manifolds
Richard G. Baraniuk and Michael B. Wakin · 2009
Cited alongside, same era.
The isotron algorithm: High-dimensional isotonic regression
Adam Tauman Kalai and Ravi Sastry · 2009
Cited alongside, same era.
On the design of loss functions for classification: theory, robustness to outliers, and savageboost
Hamed Masnadi-Shirazi and Nuno Vasconcelos · 2009
Cited alongside, same era.
B. Scholkopf and A.J. Smola · 2018
Later among the works it cites.
Optimal approximation of continuous functions by very deep relu networks
Dmitry Yarotsky · 2018
Later among the works it cites.
Localization of vc classes: Beyond local rademacher complexities
Nikita Zhivotovskiy and Steve Hanneke · 2018
Later among the works it cites.
Understanding generalization and optimization performance of deep cnns
Pan Zhou and Jiashi Feng · 2018
Later among the works it cites.
Approximation analysis of convolutional neural networks
Chenglong Bao, Qianxiao Li, Zuowei Shen, Cheng Tai, Lei Wu, and Xueshuang Xiang · 2019
Later among the works it cites.
On deep learning as a remedy for the curse of dimensionality in nonparametric regression
Benedikt Bauer and Michael Kohler · 2019
Later among the works it cites.
Fast classification rates without standard margin assumptions
Olivier Bousquet and Nikita Zhivotovskiy · 2019
Later among the works it cites.
Estimation of a function of low local dimensionality by deep neural networks
Michael Kohler, Adam Krzyzak, and Sophie Langer · 2019
Later among the works it cites.
Generalization bounds for convolutional neural networks
Shan Lin and Jingwei Zhang · 2019
Later among the works it cites.
Adaptive approximation and estimation of deep neural network with intrinsic dimensionality
Ryumei Nakada and Masaaki Imaizumi · 2019
Later among the works it cites.
Approximation and non-parametric estimation of resnet-type convolutional neural networks
Kenta Oono and Taiji Suzuki · 2019
Later among the works it cites.
Deep relu network approximation of functions on a manifold
Johannes Schmidt-Hieber · 2019
Later among the works it cites.
Nonlinear approximation via compositions
Zuowei Shen, Haizhao Yang, and Shijun Zhang · 2019
Later among the works it cites.
Theory of deep convolutional neural networks ii: Spherical analysis
Zhiying Fang, Han Feng, Shuo Huang, and Ding-Xuan Zhou · 2020
Later among the works it cites.
Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2020
Later among the works it cites.
Rejoinder: “Nonparametric regression using deep neural networks with ReLU activation function” [ MR4134775; MR4134776; MR4134777; 4134778; MR4134774]
Johannes Schmidt-Hieber · 2020
Later among the works it cites.
Deep network approximation characterized by number of neurons
Zuowei Shen, Haizhao Yang, and Shijun Zhang · 2020
Later among the works it cites.
Deep learning: a statistical viewpoint, 2021
Peter L. Bartlett, Andrea Montanari, and Alexander Rakhlin · 2021
Closest in time.
Deep neural networks for estimation and inference
Max H. Farrell, Tengyuan Liang, and Sanjog Misra · 2021
Closest in time.
Deep nonparametric regression on approximately low-dimensional manifolds, 2021
Yuling Jiao, Guohao Shen, Yuanyuan Lin, and Jian Huang · 2021
Closest in time.
Fast convergence rates of deep neural networks for classification
Yongdai Kim, Ilsang Ohn, and Dongha Kim · 2021
Closest in time.