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Representation learning that leverages large-scale labelled datasets, is central to recent progress in machine learning.
Possible principles underlying the transformation of sensory messages
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Chapter 2 building the gist of a scene: the role of global image features in recognition
Oliva, A. and Torralba, A · 2006
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Recognizing indoor scenes
Quattoni, A. and Torralba, A · 2009
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Classification and geometry of general perceptual manifolds
Chung, S., Lee, D. D., and Sompolinsky, H · 2018
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Minimum norm solutions do not always generalize well for over-parameterized problems
Shah, V., Kyrillidis, A., and Sanghavi, S · 2018
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Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Arora, S., Du, S., Hu, W., Li, Z., and Wang, R · 2019
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Similarity of neural network representations revisited
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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High-dimensional geometry of population responses in visual cortex
Stringer, C., Pachitariu, M., Steinmetz, N., Carandini, M., and Harris, K. D · 2019
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Pytorch image models
Wightman, R · 2019
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High-dimensional dynamics of generalization error in neural networks
Advani, M. S., Saxe, A. M., and Sompolinsky, H · 2020
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Benign overfitting in linear regression
Bartlett, P. L., Long, P. M., Lugosi, G., and Tsigler, A · 2020
On 1/n neural representation and robustness
Nassar, J., Sokol, P., Chang, S., and Harris, K · 2020
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Nguyen, T., Raghu, M., and Kornblith, S · 2020
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Understanding self-supervised learning with dual deep networks
Tian, Y., Yu, L., Chen, X., and Ganguli, S · 2020
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A universal law of robustness via isoperimetry
Bubeck, S. and Sellke, M · 2021
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Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data
Martin, C. H., Peng, T. S., and Mahoney, M. W · 2021
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A simple framework for contrastive learning of visual representations
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al · 2020
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Finite versus infinite neural networks: an empirical study
Lee, J., Schoenholz, S., Pennington, J., Adlam, B., Xiao, L., Novak, R., and Sohl-Dickstein, J · 2020
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Do vision transformers see like convolutional neural networks?
Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., and Dosovitskiy, A · 2021
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Covariate shift in high-dimensional random feature regression
Tripuraneni, N., Adlam, B., and Pennington, J · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S · 2021
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Increasing neural network robustness improves match to macaque v1 eigenspectrum, spatial frequency preference and predictivity
Kong, N. C., Margalit, E., Gardner, J. L., and Norcia, A. M · 2022
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