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Benefits of Jointly Training Autoencoders: An Improved Neural Tangent
  Kernel Analysis
v1v2 (latest)

Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis

IEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2019
27 November 2019
THANH VAN NGUYEN
Raymond K. W. Wong
Chinmay Hegde
ArXiv (abs)PDFHTML

Papers citing "Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis"

5 / 5 papers shown
Title
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
Simone Brivio
Nicola Rares Franco
101
0
0
13 Jun 2025
Non-Parametric Representation Learning with Kernels
Non-Parametric Representation Learning with KernelsAAAI Conference on Artificial Intelligence (AAAI), 2023
Pascal Esser
Maximilian Fleissner
Debarghya Ghoshdastidar
SSL
255
11
0
05 Sep 2023
High-dimensional Asymptotics of Denoising Autoencoders
High-dimensional Asymptotics of Denoising AutoencodersNeural Information Processing Systems (NeurIPS), 2023
Hugo Cui
Lenka Zdeborová
152
17
0
18 May 2023
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with
  Gradient Methods
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient MethodsInternational Conference on Machine Learning (ICML), 2022
Aleksandr Shevchenko
Kevin Kögler
Hamed Hassani
Marco Mondelli
DRLMLT
139
3
0
27 Dec 2022
Toward Understanding the Feature Learning Process of Self-supervised
  Contrastive Learning
Toward Understanding the Feature Learning Process of Self-supervised Contrastive LearningInternational Conference on Machine Learning (ICML), 2021
Zixin Wen
Yuanzhi Li
SSLMLT
327
150
0
31 May 2021
1