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2008.02447
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Functional Regularization for Representation Learning: A Unified Theoretical Perspective
6 August 2020
Siddhant Garg
Yingyu Liang
SSL
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Papers citing
"Functional Regularization for Representation Learning: A Unified Theoretical Perspective"
13 / 13 papers shown
Title
A Quantitative Approach to Predicting Representational Learning and Performance in Neural Networks
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Learning with Explanation Constraints
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Provable Pathways: Learning Multiple Tasks over Multiple Paths
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The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning
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Jiefeng Chen
Kunyang Li
Jayaram Raghuram
Xi Wu
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S. Jha
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71
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Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning
Thanh-Dung Le
R. Noumeir
J. Rambaud
Guillaume Sans
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68
9
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26 Sep 2022
Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching
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Hongyu Guo
Jian Tang
113
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27 Jun 2022
Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey
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Issei Sato
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134
5
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18 Apr 2022
A Framework of Meta Functional Learning for Regularising Knowledge Transfer
Pan Li
Yanwei Fu
S. Gong
32
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0
28 Mar 2022
Provable and Efficient Continual Representation Learning
Yingcong Li
Mingchen Li
M. Salman Asif
Samet Oymak
CLL
59
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0
03 Mar 2022
Sample Efficiency of Data Augmentation Consistency Regularization
Shuo Yang
Yijun Dong
Rachel A. Ward
Inderjit S. Dhillon
Sujay Sanghavi
Qi Lei
AAML
84
17
0
24 Feb 2022
Pre-training Molecular Graph Representation with 3D Geometry
Shengchao Liu
Hanchen Wang
Weiyang Liu
Joan Lasenby
Hongyu Guo
Jian Tang
197
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07 Oct 2021
On the Surrogate Gap between Contrastive and Supervised Losses
Han Bao
Yoshihiro Nagano
Kento Nozawa
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77
22
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06 Oct 2021
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
Kento Nozawa
Issei Sato
SSL
132
46
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13 Feb 2021
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