/Contents 14 0 R Exploring the Memorization-Generalization Continuum in Deep Learning. /Contents 157 0 R 10 0 obj Exploring generalization in deep learning. /Resources 51 0 R

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/MediaBox [ 0 0 612 792 ] /Parent 1 0 R /Parent 1 0 R /Length 3663 We study how these measures can ensure generalization, highlighting the importance of scale normalization, and making a connection between sharpness and PAC-Bayes theory. 7 0 obj endobj /Resources 399 0 R /Contents 109 0 R endobj Title: Exploring Generalization in Deep Learning. Authors: Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro. /Parent 1 0 R

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Behnam Neyshabur ... We study how these measures can ensure generalization, highlighting the importance of scale normalization, and making a connection between sharpness and PAC-Bayes theory. Previous Chapter Next Chapter. /Resources 15 0 R x�}Zϗ� ��_��ތ*R�{iw�I�m�n�ӗ�d��++K�$g2����(K����$"A >��6ϛh��H��~�p�6j��b�GEX�tS�1.�M�E������?޼{x�o�x��P�4�< << endobj /Contents 462 0 R

Human learners appreciate that some facts demand memorization whereas other facts support generalization.

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/Parent 1 0 R /MediaBox [ 0 0 612 792 ] << /Type /Page endobj /MediaBox [ 0 0 612 792 ] We then investigate how well the measures explain different observed phenomena.Papers published at the Neural Information Processing Systems Conference.

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6 0 obj Exploring Generalization in Deep Learning Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro Toyota Technological Institute at Chicago {bneyshabur, srinadh, mcallester, nati}@ttic.edu Abstract With a goal of understanding what drives generalization in deep networks, we 9 0 obj

/Annots [ 119 0 R 120 0 R 121 0 R 122 0 R 123 0 R 124 0 R 125 0 R 126 0 R 127 0 R 128 0 R 129 0 R 130 0 R 131 0 R ] With a goal of understanding what drives generalization in deep networks, we consider several recently suggested explanations, including norm-based control, sharpness and robustness. /Contents 50 0 R << Exploring Generalization in Deep Learning Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro Toyota Technological Institute at Chicago {bneyshabur, srinadh, mcallester, nati}@ttic.edu Abstract With a goal of understanding what drives generalization in deep networks, we 12 0 obj /Type /Page

14 0 obj /Annots [ 29 0 R 30 0 R 31 0 R 32 0 R 33 0 R 34 0 R 35 0 R 36 0 R 37 0 R 38 0 R 39 0 R 40 0 R 41 0 R 42 0 R 43 0 R 44 0 R 45 0 R 46 0 R 47 0 R 48 0 R 49 0 R ] Bibliographic details on Exploring Generalization in Deep Learning. /Publisher (Curran Associates\054 Inc\056) /Type /Page 02/08/2020 ∙ by Ziheng Jiang, et al. /Type (Conference Proceedings) /Contents 460 0 R We discuss advantages and weaknesses of each of these complexity measures and examine their abilities to explain the observed generalization phenomena in deep learning. With a goal of understanding what drives generalization in deep networks, we consider several recently suggested explanations, including norm-based control, sharpness and robustness. << /Resources 226 0 R Pages 5949–5958.

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Part of: Advances in Neural Information Processing Systems 30 (NIPS 2017) [Supplemental] Authors. Exploring Generalization in Deep Learning. /Author (Behnam Neyshabur\054 Srinadh Bhojanapalli\054 David Mcallester\054 Nati Srebro)

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/MediaBox [ 0 0 612 792 ] /Parent 1 0 R /Contents 323 0 R In view of the current Corona Virus epidemic, Schloss Dagstuhl has moved its 2020 proposal submission period to July 1 to July 15, 2020 , and there will not be another proposal round in November 2020. endobj >> << /Parent 1 0 R /Contents 398 0 R /Title (Exploring Generalization in Deep Learning) 2 Generalization and Capacity Control in Deep Learning In this section, we discuss complexity measures that have been suggested, or could be used for capacity control in neural networks. stream

/Count 10 /Editors (I\056 Guyon and U\056V\056 Luxburg and S\056 Bengio and H\056 Wallach and R\056 Fergus and S\056 Vishwanathan and R\056 Garnett) %PDF-1.3 With a goal of understanding what drives generalization in deep networks, we consider several recently suggested explanations, including norm-based control, sharpness and robustness. /Annots [ 390 0 R 391 0 R 392 0 R 393 0 R 394 0 R 395 0 R 396 0 R 397 0 R ] /Date (2017)

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