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1905.00414
Cited By
Similarity of Neural Network Representations Revisited
1 May 2019
Simon Kornblith
Mohammad Norouzi
Honglak Lee
Geoffrey E. Hinton
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Papers citing
"Similarity of Neural Network Representations Revisited"
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Title
Diffused Redundancy in Pre-trained Representations
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41
19
0
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26
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Investigating the Effects of Fairness Interventions Using Pointwise Representational Similarity
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Till Speicher
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Mariya Toneva
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30
1
0
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G. Rochette
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37
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0
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Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
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Alexander B. Atanasov
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Cengiz Pehlevan
32
23
0
28 May 2023
A Three-regime Model of Network Pruning
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Yaoqing Yang
Arin Chang
Michael W. Mahoney
39
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0
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On the special role of class-selective neurons in early training
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Nikhil Thakurdesai
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Matthew L. Leavitt
Stéphane Deny
22
2
0
27 May 2023
Emergent representations in networks trained with the Forward-Forward algorithm
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Lorenzo Basile
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31
9
0
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Jianyuan Guo
Kai Han
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Yunhe Wang
38
16
0
25 May 2023
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning
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Yipeng Zhou
Gang Liu
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Shui Yu
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30
14
0
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Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource Settings
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On Robustness of Finetuned Transformer-based NLP Models
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32
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0
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41
1
0
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Similarity of Neural Network Models: A Survey of Functional and Representational Measures
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Tobias Schumacher
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Florian Lemmerich
63
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SRIL: Selective Regularization for Class-Incremental Learning
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35
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S. Dasmahapatra
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ZipIt! Merging Models from Different Tasks without Training
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52
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0
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32
8
0
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Identifying the Correlation Between Language Distance and Cross-Lingual Transfer in a Multilingual Representation Space
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Discovering the Effectiveness of Pre-Training in a Large-scale Car-sharing Platform
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25
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Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement
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Bryan Hooi
49
6
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Redundancy and Concept Analysis for Code-trained Language Models
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Ali Jannesari
80
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Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removal
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Elahe Arani
32
2
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Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient
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Youngeun Kim
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Priyadarshini Panda
34
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Objectives Matter: Understanding the Impact of Self-Supervised Objectives on Vision Transformer Representations
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Florian Bordes
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Ari S. Morcos
29
10
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Distilling from Similar Tasks for Transfer Learning on a Budget
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Cheng Perng Phoo
Bharath Hariharan
30
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How good are variational autoencoders at transfer learning?
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M. Grzes
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23
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False Claims against Model Ownership Resolution
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S. Szyller
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Nirmal Asokan
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31
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ADS_UNet: A Nested UNet for Histopathology Image Segmentation
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34
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Interpretable statistical representations of neural population dynamics and geometry
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Alexis Arnaudon
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17
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Source-free Domain Adaptation Requires Penalized Diversity
Laya Rafiee Sevyeri
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24
0
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What Affects Learned Equivariance in Deep Image Recognition Models?
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31
7
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46
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Cross-Class Feature Augmentation for Class Incremental Learning
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Asymmetric Image Retrieval with Cross Model Compatible Ensembles
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Preserving Linear Separability in Continual Learning by Backward Feature Projection
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Deep Augmentation: Dropout as Augmentation for Self-Supervised Learning
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A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias
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Continual Learning in the Presence of Spurious Correlation
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