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Similarity metrics such as representational similarity analysis (RSA) and centered kernel alignment (CKA) have been used to compare layer-wise representations between neural networks.
Testing for serial correlation in least squares regression. i
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Distributed and overlapping representations of faces and objects in ventral temporal cortex
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Imagenet classification with deep convolutional neural networks
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
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Understanding intermediate layers using linear classifier probes
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Deep Learning
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He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Moment matching for multi-source domain adaptation
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Efficientnet: Rethinking model scaling for convolutional neural networks
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Unsupervised cross-lingual representation learning at scale
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Feng, Y., Zhai, R., He, D., Wang, L., and Dong, B · 2020
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Covariate-adjusted Spearman’s rank correlation with probability-scale residuals
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Decoupled weight decay regularization
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Insights on representational similarity in neural networks with canonical correlation
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Failing loudly: An empirical study of methods for detecting dataset shift
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Covariate-adjusted heatmaps for visualizing biological data via correlation decomposition
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Individual differences among deep neural network models
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What is being transferred in transfer learning?
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Grounding representation similarity with statistical testing
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Why do better loss functions lead to less transferable features?
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Machine translated multilingual STS benchmark dataset
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Do vision transformers see like convolutional neural networks?
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Using distance on the Riemannian manifold to compare representations in brain and in models
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Generalized shape metrics on neural representations
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