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Functional Regularization for Representation Learning: A Unified
  Theoretical Perspective
v1v2v3 (latest)

Functional Regularization for Representation Learning: A Unified Theoretical Perspective

6 August 2020
Siddhant Garg
Yingyu Liang
    SSL
ArXiv (abs)PDFHTML

Papers citing "Functional Regularization for Representation Learning: A Unified Theoretical Perspective"

14 / 14 papers shown
An Augmentation Overlap Theory of Contrastive Learning
An Augmentation Overlap Theory of Contrastive Learning
Qi Zhang
Yifei Wang
Yisen Wang
SSL
251
4
0
05 Nov 2025
A Quantitative Approach to Predicting Representational Learning and
  Performance in Neural Networks
A Quantitative Approach to Predicting Representational Learning and Performance in Neural Networks
Ryan Pyle
Sebastian Musslick
Jonathan D. Cohen
Ankit B. Patel
194
0
0
14 Jul 2023
Learning with Explanation Constraints
Learning with Explanation ConstraintsNeural Information Processing Systems (NeurIPS), 2023
Rattana Pukdee
Dylan Sam
J. Zico Kolter
Maria-Florina Balcan
Pradeep Ravikumar
FAtt
377
10
0
25 Mar 2023
Provable Pathways: Learning Multiple Tasks over Multiple Paths
Provable Pathways: Learning Multiple Tasks over Multiple PathsAAAI Conference on Artificial Intelligence (AAAI), 2023
Yingcong Li
Samet Oymak
MoE
238
4
0
08 Mar 2023
The Trade-off between Universality and Label Efficiency of
  Representations from Contrastive Learning
The Trade-off between Universality and Label Efficiency of Representations from Contrastive LearningInternational Conference on Learning Representations (ICLR), 2023
Zhenmei Shi
Jiefeng Chen
Kunyang Li
Jayaram Raghuram
Xi Wu
Yingyu Liang
S. Jha
SSL
216
28
0
28 Feb 2023
Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes
  Representation Learning
Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation LearningIEEE Journal of Translational Engineering in Health and Medicine (IEEE JTEHM), 2022
Thanh-Dung Le
R. Noumeir
J. Rambaud
Guillaume Sans
P. Jouvet
248
12
0
26 Sep 2022
Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance
  Matching
Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance MatchingInternational Conference on Learning Representations (ICLR), 2022
Shengchao Liu
Ziqiao Wang
Jian Tang
351
99
0
27 Jun 2022
Empirical Evaluation and Theoretical Analysis for Representation
  Learning: A Survey
Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey
Kento Nozawa
Issei Sato
AI4TS
343
5
0
18 Apr 2022
A Framework of Meta Functional Learning for Regularising Knowledge
  Transfer
A Framework of Meta Functional Learning for Regularising Knowledge Transfer
Pan Li
Yanwei Fu
S. Gong
141
0
0
28 Mar 2022
Provable and Efficient Continual Representation Learning
Provable and Efficient Continual Representation Learning
Yingcong Li
Mingchen Li
M. Salman Asif
Samet Oymak
CLL
297
16
0
03 Mar 2022
Sample Efficiency of Data Augmentation Consistency Regularization
Sample Efficiency of Data Augmentation Consistency RegularizationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Shuo Yang
Yijun Dong
Rachel A. Ward
Inderjit S. Dhillon
Sujay Sanghavi
Qi Lei
AAML
196
21
0
24 Feb 2022
Pre-training Molecular Graph Representation with 3D Geometry
Pre-training Molecular Graph Representation with 3D Geometry
Shengchao Liu
Hanchen Wang
Weiyang Liu
Joan Lasenby
Ziqiao Wang
Jian Tang
498
431
0
07 Oct 2021
On the Surrogate Gap between Contrastive and Supervised Losses
On the Surrogate Gap between Contrastive and Supervised Losses
Han Bao
Yoshihiro Nagano
Kento Nozawa
SSLUQCV
392
27
0
06 Oct 2021
Understanding Negative Samples in Instance Discriminative
  Self-supervised Representation Learning
Understanding Negative Samples in Instance Discriminative Self-supervised Representation LearningNeural Information Processing Systems (NeurIPS), 2021
Kento Nozawa
Issei Sato
SSL
550
53
0
13 Feb 2021
1
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