exploring generalization in deep learning

Bibliographic details on Exploring Generalization in Deep Learning. We discuss advantages and weaknesses of each of these complexity measures and examine their abilities to explain the observed generalization phenomena in deep learning.

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/Type (Conference Proceedings) stream Human learners appreciate that some facts demand memorization whereas other facts support generalization. >>
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/Type /Page /firstpage (5947) We study how these measures can ensure generalization, highlighting the importance of scale normalization, and making a connection between sharpness and PAC-Bayes theory. << /Parent 1 0 R /MediaBox [ 0 0 612 792 ] /Contents 157 0 R

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/Parent 1 0 R /Kids [ 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R 13 0 R ] /Filter /FlateDecode 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.

>> /Resources 51 0 R /Type /Page 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.

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ABSTRACT. We study how these measures can ensure generalization, highlighting the importance of scale normalization, and making a connection between sharpness and PAC-Bayes theory. <<

>> endobj /Contents 132 0 R /MediaBox [ 0 0 612 792 ] /MediaBox [ 0 0 612 792 ] With a goal of understanding what drives generalization in deep networks, we consider several recently suggested explanations, including norm-based control, sharpness and robustness. 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�<


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 158 0 R

/Type /Catalog endobj /Type /Page >> /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) Exploring generalization in deep learning. /Contents 398 0 R Title: Exploring Generalization in Deep Learning.

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Part of: Advances in Neural Information Processing Systems 30 (NIPS 2017) [Supplemental] Authors. Exploring Generalization in Deep Learning. /Annots [ 219 0 R 220 0 R 221 0 R 222 0 R 223 0 R 224 0 R ] << Previous Chapter Next Chapter.

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/Publisher (Curran Associates\054 Inc\056) 12 0 obj 8 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 /lastpage (5956)

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Exploring the Memorization-Generalization Continuum in Deep Learning.

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02/08/2020 ∙ by Ziheng Jiang, et al.

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/Contents 50 0 R /Parent 1 0 R /Description-Abstract (With a goal of understanding what drives generalization in deep networks\054 we consider several recently suggested explanations\054 including norm\055based control\054 sharpness and robustness\056 We study how these measures can ensure generalization\054 highlighting the importance of scale normalization\054 and making a connection between sharpness and PAC\055Bayes theory\056 We then investigate how well the measures explain different observed phenomena\056) endobj >>

/Count 10 /MediaBox [ 0 0 612 792 ] /EventType (Poster) /Author (Behnam Neyshabur\054 Srinadh Bhojanapalli\054 David Mcallester\054 Nati Srebro)

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