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Transfer learning seeks to improve the generalization performance of a target task by exploiting the knowledge learned from a related source task.
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J. Yang, R. Yan, and A. G. Hauptmann, “Adapting svm classifiers to data with shifted distributions,” in Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007) , 2007, pp. 69–76
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M. Rudelson and R. Vershynin, “Non-asymptotic theory of random matrices: extreme singular values,” 2010
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M. Stojnic, “A framework to characterize performance of lasso algorithms,” 2013
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2018
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2018
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S. Kornblith, J. Shlens, and Q. V. Le, “Do better imagenet models transfer better?” 2019
2019
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2019
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J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” 2014
2014
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2015
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2016
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S. Adachi, S. Iwata, Y. Nakatsukasa, and A. Takeda, “Solving the trust-region subproblem by a generalized eigenvalue problem,” SIAM Journal on Optimization , vol. 27, no. 1, pp. 269–291, 2017. [Online]. Available: https://doi.org/10.1137/16M1058200
2017
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2019
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Y. Dar and R. G. Baraniuk, “Double double descent: On generalization errors in transfer learning between linear regression tasks,” 2020
2020
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L. Saglietti and L. Zdeborová, “Solvable model for inheriting the regularization through knowledge distillation,” 2020
2020
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O. Dhifallah and Y. M. Lu, “A precise performance analysis of learning with random features,” 2020
2020
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A. Kammoun and M.-S. Alouini, “On the precise error analysis of support vector machines,” 2020
2020
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F. Mignacco, F. Krzakala, Y. M. Lu, and L. Zdeborová, “The role of regularization in classification of high-dimensional noisy gaussian mixture,” 2020
2020
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B. Aubin, F. Krzakala, Y. M. Lu, and L. Zdeborová, “Generalization error in high-dimensional perceptrons: Approaching bayes error with convex optimization,” 2020
2020
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