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Deep Learning and Structured Data
Başlık:
Deep Learning and Structured Data
Yazar:
Zhang, Chiyuan, author.
Yazar Ek Girişi:
Genel Not:
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Advisors: Tomaso Poggio.
Özet:
In the recent years deep learning has witnessed successful applications in many different domains such as visual object recognition, detection and segmentation, automatic speech recognition, natural language processing, and reinforcement learning. In this thesis, we will investigate deep learning from a spectrum of different perspectives.
First of all, we will study the question of generalization, which is one of the most fundamental notion in machine learning theory. We will show how, in the regime of deep learning, the characterization of generalization becomes different from the conventional way, and propose alternative ways to approach it.
Moving from theory to more practical perspectives, we will show two different applications of deep learning. One is originated from a real world problem of automatic geophysical feature detection from seismic recordings to help oil & gas exploration; the other is motivated from a computational neuroscientific modeling and studying of human auditory system. More specifically, we will show how deep learning could be adapted to play nicely with the unique structures associated with the problems from different domains.
Lastly, we move to the computer system design perspective, and present our efforts in building better deep learning systems to allow efficient and flexible computation in both academic and industrial worlds. (Copies available exclusively from MIT Libraries, libraries.mit.edu/docs - docs mit.edu).
Notlar:
School code: 0753
Tüzel Kişi Ek Girişi:
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Yer Numarası | Demirbaş Numarası | Shelf Location | Lokasyon / Statüsü / İade Tarihi |
---|---|---|---|
XX(687435.1) | 687435-1001 | Proquest E-Tez Koleksiyonu | Arıyor... |
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