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Measuring similarity of neural networks to understand and improve their behavior has become an issue of great importance and research interest.
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Howard. Tinsley and David. Weiss · 1975
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“A Unifying Tool for Linear Multivariate Statistical Methods: The RV- Coefficient”
Paul Robert and Yves Escoufier · 1976
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“A Unifying Tool for Linear Multivariate Statistical Methods: The RV- Coefficient”
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“Misinterpretation And Misuse Of The Kappa Statistic”
Malcom Maclure and Walter. Willett · 1987
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“Misinterpretation And Misuse Of The Kappa Statistic”
Malcom Maclure and Walter. Willett · 1987
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Jianhua Lin · 1991
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“Divergence measures based on the Shannon entropy”
Jianhua Lin · 1991
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“Bias, prevalence and kappa”
Ted Byrt, Janet Bishop and John. Carlin · 1993
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“Building a large annotated corpus of English: the penn treebank”
Mitchell. Marcus, Mary Marcinkiewicz and Beatrice Santorini · 1993
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“Bias, prevalence and kappa”
Ted Byrt, Janet Bishop and John. Carlin · 1993
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“Building a large annotated corpus of English: the penn treebank”
Mitchell. Marcus, Mary Marcinkiewicz and Beatrice Santorini · 1993
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“The sources of increased accuracy for two proposed boosting algorithms”
David Skalak · 1996
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“Detecting Sequential Patterns and Determining Their Reliability With Fallible Observers”
Roger Bakeman, Vicenq Quera, Duncan McArthur and Byron Robinson · 1997
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“Detecting Sequential Patterns and Determining Their Reliability With Fallible Observers”
Roger Bakeman, Vicenq Quera, Duncan McArthur and Byron Robinson · 1997
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“Beyond kappa: A review of interrater agreement measures”
Mousumi Banerjee, Michelle Capozzoli, Laura McSweeney and Debajyoti Sinha · 1999
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“Beyond kappa: A review of interrater agreement measures”
Mousumi Banerjee, Michelle Capozzoli, Laura McSweeney and Debajyoti Sinha · 1999
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“On Kernel-Target Alignment”
Nello Cristianini, John Shawe-Taylor, André Elisseeff and Jaz. Kandola · 2001
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“On Kernel-Target Alignment”
Nello Cristianini, John Shawe-Taylor, André Elisseeff and Jaz. Kandola · 2001
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“Community structure in social and biological networks”
Michelle Girvan and Mark.. Newman · 2002
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Michelle Girvan and Mark.. Newman · 2002
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“Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy”
Ludmila. Kuncheva and Christopher. Whitaker · 2003
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“Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy”
Ludmila. Kuncheva and Christopher. Whitaker · 2003
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“Co-Validation: Using Model Disagreement on Unlabeled Data to Validate Classification Algorithms”
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“Co-Validation: Using Model Disagreement on Unlabeled Data to Validate Classification Algorithms”
Omid Madani, David. Pennock and Gary Flake · 2004
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“Diversity Creation Methods: A Survey And Categorisation”
Gavin Brown, Jeremy Wyatt, Rachel Harris and Xin Yao · 2005
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“Measuring Statistical Dependence with Hilbert-Schmidt Norms”
Arthur Gretton, Olivier Bousquet, Alex Smola and Bernhard Schölkopf · 2005
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“Algebraic topology”, 2005
Allen Hatcher · 2005
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“The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requirements”
Julius Sim and Chris Wright · 2005
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“Diversity Creation Methods: A Survey And Categorisation”
Gavin Brown, Jeremy Wyatt, Rachel Harris and Xin Yao · 2005
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“Measuring Statistical Dependence with Hilbert-Schmidt Norms”
Arthur Gretton, Olivier Bousquet, Alex Smola and Bernhard Schölkopf · 2005
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“Algebraic topology”, 2005
Allen Hatcher · 2005
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“The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requirements”
Julius Sim and Chris Wright · 2005
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“Modularity and community structure in networks”
Mark.. Newman · 2006
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“An analysis of diversity measures”
Ke Tang, Ponnuthurai. Suganthan and Xin Yao · 2006
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“Modularity and community structure in networks”
Mark.. Newman · 2006
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“An analysis of diversity measures”
Ke Tang, Ponnuthurai. Suganthan and Xin Yao · 2006
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“Positive definite matrices”, Princeton series in applied mathematics, 2007
Rajendra Bhatia · 2007
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“Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions”
Sung-Hyuk Cha · 2007
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“How to generate random matrices from the classical compact groups”
Francesco Mezzadri · 2007
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“Measuring and testing dependence by correlation of distances”
Gábor. Székely, Maria. Rizzo and Nail. Bakirov · 2007
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“Positive definite matrices”, Princeton series in applied mathematics, 2007
Rajendra Bhatia · 2007
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“Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions”
Sung-Hyuk Cha · 2007
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“How to generate random matrices from the classical compact groups”
Francesco Mezzadri · 2007
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“Measuring and testing dependence by correlation of distances”
Gábor. Székely, Maria. Rizzo and Nail. Bakirov · 2007
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“Representational similarity analysis - connecting the branches of systems neuroscience”
Nikolaus Kriegeskorte, Marieke Mur and Peter Bandettini · 2008
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“Representational similarity analysis - connecting the branches of systems neuroscience”
Nikolaus Kriegeskorte, Marieke Mur and Peter Bandettini · 2008
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“ImageNet: A Large-Scale Hierarchical Image Database”
Jia Deng et al · 2009
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“Learning Multiple Layers of Features from Tiny Images”, 2009
Alex Krizhevsky · 2009
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“ImageNet: A Large-Scale Hierarchical Image Database”
Jia Deng et al · 2009
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“Learning Multiple Layers of Features from Tiny Images”, 2009
Alex Krizhevsky · 2009
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“Reading digits in natural images with unsupervised feature learning”
Yuval Netzer et al · 2011
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“Reading digits in natural images with unsupervised feature learning”
Yuval Netzer et al · 2011
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“Algorithms for learning kernels based on centered alignment”
Corinna Cortes, Mehryar Mohri and Afshin Rostamizadeh · 2012
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“ImageNet Classification with Deep Convolutional Neural Networks”
Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton · 2012
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“Feature Selection via Dependence Maximization”
Le Song et al · 2012
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“Algorithms for learning kernels based on centered alignment”
Corinna Cortes, Mehryar Mohri and Afshin Rostamizadeh · 2012
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“ImageNet Classification with Deep Convolutional Neural Networks”
Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton · 2012
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“Feature Selection via Dependence Maximization”
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“Combinatorics and geometry of transportation polytopes: An update.”
Jesús De and Edward Kim · 2013
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“Sparse subspace clustering: Algorithm, theory, and applications”
Ehsan Elhamifar and René Vidal · 2013
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“Interrater agreement and interrater reliability: Key concepts, approaches, and applications”
Natasa Gisev, J. Bell and Timothy. Chen · 2013
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Brian Kulis · 2013
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“Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps”
Karen Simonyan, Andrea Vedaldi and Andrew Zisserman · 2013
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“Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank”
Richard Socher et al · 2013
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“Combinatorics and geometry of transportation polytopes: An update.”
Jesús De and Edward Kim · 2013
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“Sparse subspace clustering: Algorithm, theory, and applications”
Ehsan Elhamifar and René Vidal · 2013
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“Interrater agreement and interrater reliability: Key concepts, approaches, and applications”
Natasa Gisev, J. Bell and Timothy. Chen · 2013
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“Metric learning: A survey”
Brian Kulis · 2013
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“Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps”
Karen Simonyan, Andrea Vedaldi and Andrew Zisserman · 2013
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“Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank”
Richard Socher et al · 2013
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“Do deep nets really need to be deep?”
Jimmy Ba and Rich Caruana · 2014
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“Findings of the 2014 Workshop on Statistical Machine Translation”
Ondřej Bojar et al · 2014
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“Mistakes and how to avoid mistakes in using intercoder reliability indices.”
Guangchao Feng · 2014
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“Auto-Encoding Variational Bayes”
Diederik. Kingma and Max Welling · 2014
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“Striving for simplicity: The all convolutional net”
Jost Springenberg, Alexey Dosovitskiy, Thomas Brox and Martin Riedmiller · 2014
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“Do deep nets really need to be deep?”
Jimmy Ba and Rich Caruana · 2014
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“Findings of the 2014 Workshop on Statistical Machine Translation”
Ondřej Bojar et al · 2014
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“Mistakes and how to avoid mistakes in using intercoder reliability indices.”
Guangchao Feng · 2014
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“Auto-Encoding Variational Bayes”
Diederik. Kingma and Max Welling · 2014
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“Striving for simplicity: The all convolutional net”
Jost Springenberg, Alexey Dosovitskiy, Thomas Brox and Martin Riedmiller · 2014
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“Understanding image representations by measuring their equivariance and equivalence”
Karel Lenc and Andrea Vedaldi · 2015
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Aravindh Mahendran and Andrea Vedaldi · 2015
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K Simonyan and A Zisserman · 2015
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Christian Szegedy et al · 2015
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Karel Lenc and Andrea Vedaldi · 2015
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Aravindh Mahendran and Andrea Vedaldi · 2015
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“Very deep convolutional networks for large-scale image recognition”
K Simonyan and A Zisserman · 2015
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“Going deeper with convolutions”
Christian Szegedy et al · 2015
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“Intrinsic dimension estimation: Advances and open problems”
Francesco Camastra and Antonino Staiano · 2016
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“Launch and Iterate: Reducing Prediction Churn”
Mahdi Fard, Quentin Cormier, Kevin Canini and Maya. Gupta · 2016
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“Deep learning”, 2016
Ian Goodfellow, Yoshua Bengio and Aaron Courville · 2016
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“Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change”
William. Hamilton, Jure Leskovec and Dan Jurafsky · 2016
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“Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change”
William. Hamilton, Jure Leskovec and Dan Jurafsky · 2016
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“Deep Residual Learning for Image Recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
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“Convergent Learning: Do different neural networks learn the same representations?”
Yixuan Li et al · 2016
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“Revisiting Semi-Supervised Learning with Graph Embeddings”
Zhilin Yang, William Cohen and Ruslan Salakhudinov · 2016
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“Intrinsic dimension estimation: Advances and open problems”
Francesco Camastra and Antonino Staiano · 2016
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“Launch and Iterate: Reducing Prediction Churn”
Mahdi Fard, Quentin Cormier, Kevin Canini and Maya. Gupta · 2016
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“Deep learning”, 2016
Ian Goodfellow, Yoshua Bengio and Aaron Courville · 2016
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“Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change”
William. Hamilton, Jure Leskovec and Dan Jurafsky · 2016
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“Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change”
William. Hamilton, Jure Leskovec and Dan Jurafsky · 2016
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“Deep Residual Learning for Image Recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
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“Convergent Learning: Do different neural networks learn the same representations?”
Yixuan Li et al · 2016
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“Revisiting Semi-Supervised Learning with Graph Embeddings”
Zhilin Yang, William Cohen and Ruslan Salakhudinov · 2016
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AG Howard · 2017
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Stephen Merity, Caiming Xiong, James Bradbury and Richard Socher · 2017
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“SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability”
Maithra Raghu, Justin Gilmer, Jason Yosinski and Jascha Sohl-Dickstein · 2017
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“Fast Estimation of $tr(f(A))$ via Stochastic Lanczos Quadrature”
Shashanka Ubaru, Jie Chen and Yousef Saad · 2017
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“Inductive Representation Learning on Large Graphs”
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“Mobilenets: Efficient convolu-tional neural networks for mobile vision applications”
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“Pointer Sentinel Mixture Models”
Stephen Merity, Caiming Xiong, James Bradbury and Richard Socher · 2017
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“SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability”
Maithra Raghu, Justin Gilmer, Jason Yosinski and Jascha Sohl-Dickstein · 2017
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“Fast Estimation of $tr(f(A))$ via Stochastic Lanczos Quadrature”
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“All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality”
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“Generalized Shape Metrics on Neural Representations”
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“A Survey on Canonical Correlation Analysis”
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“TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness”
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“Representation Topology Divergence: A Method for Comparing Neural Network Representations”
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“Probing Classifiers: Promises, Shortcomings, and Advances”
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“GULP: a prediction-based metric between representations”
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“Deconfounded Representation Similarity for Comparison of Neural Networks”
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“On the Inadequacy of CKA as a Measure of Similarity in Deep Learning”
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“The pitfalls of measuring representational similarity using representational similarity analysis”
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“Vision-language pre-training: Basics, recent advances, and future trends”
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“On the Symmetries of Deep Learning Models and their Internal Representations”
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“Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning”
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“Rashomon Capacity: A Metric for Predictive Multiplicity in Classification”
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“If you’ve trained one you’ve trained them all: inter-architecture similarity increases with robustness”
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“On the Prediction Instability of Graph Neural Networks”
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“A ConvNet for the 2020s”
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