Understanding Augmentation-based Self-Supervised Representation Learning
via RKHS Approximation and Regression

Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression

1 June 2023

Andrej Risteski

Pradeep Ravikumar

Papers citing "Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression"

9 / 9 papers shown

Title
Contextures: Representations from Contexts Runtian Zhai Kai Yang Che-Ping Tsai Burak Varici Zico Kolter Pradeep Ravikumar 32 0 0 02 May 2025
InfoNCE: Identifying the Gap Between Theory and Practice E. Rusak Patrik Reizinger Attila Juhos Oliver Bringmann Roland S. Zimmermann Wieland Brendel 33 5 0 28 Jun 2024
Spectral Representation Learning for Conditional Moment Models Ziyu Wang Yucen Luo Yueru Li Jun Zhu Bernhard Schölkopf CML 23 7 0 29 Oct 2022
The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima Peter L. Bartlett Philip M. Long Olivier Bousquet 63 34 0 04 Oct 2022
On the Principles of Parsimony and Self-Consistency for the Emergence of Intelligence Y. Ma Doris Y. Tsao H. Shum 52 75 0 11 Jul 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 258 7,337 0 11 Nov 2021
Understanding self-supervised Learning Dynamics without Contrastive Pairs Yuandong Tian Xinlei Chen Surya Ganguli SSL 132 278 0 12 Feb 2021
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 294 6,927 0 20 Apr 2018
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 273 2,878 0 15 Sep 2016