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020 ▼a 9780438291706
035 ▼a (MiAaPQ)AAI10843897
035 ▼a (MiAaPQ)ucla:17157
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 001.5
1001 ▼a Chaudhari, Pratik Anil.
24512 ▼a A Picture of the Energy Landscape of Deep Neural Networks.
260 ▼a [S.l.] : ▼b University of California, Los Angeles., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 175 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
500 ▼a Adviser: Stefano Soatto.
5021 ▼a Thesis (Ph.D.)--University of California, Los Angeles, 2018.
520 ▼a This thesis characterizes the training process of deep neural networks. We are driven by two apparent paradoxes. First, optimizing a non-convex function such as the loss function of a deep network should be extremely hard, yet rudimentary algori
520 ▼a We build upon tools from two main areas to make progress on these questions: statistical physics and a continuous-time point-of-view of optimization. The former has been popular in the study of machine learning in the past and has been rejuvenat
520 ▼a The confluence of these ideas leads to fundamental theoretical insights that explain observed phenomena in deep learning as well as the development of state-of-the-art algorithms for training deep networks.
590 ▼a School code: 0031.
650 4 ▼a Artificial intelligence.
650 4 ▼a Applied mathematics.
650 4 ▼a Statistical physics.
690 ▼a 0800
690 ▼a 0364
690 ▼a 0217
71020 ▼a University of California, Los Angeles. ▼b Computer Science 0201.
7730 ▼t Dissertation Abstracts International ▼g 80-01B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0031
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999954 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자