東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Dealings with Data: Physics, Machine...

Hayden, Lorien Xanthe.

FindBook

Google Book

Amazon

博客來

Dealings with Data: Physics, Machine Learning and Geometry.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dealings with Data: Physics, Machine Learning and Geometry./
作者:	Hayden, Lorien Xanthe.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	149 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Contained By:	Dissertations Abstracts International81-04B.
標題:	Computational physics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22616491
ISBN:	9781088392591

Dealings with Data: Physics, Machine Learning and Geometry.
Hayden, Lorien Xanthe.

Dealings with Data: Physics, Machine Learning and Geometry. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 149 p.

Source: Dissertations Abstracts International, Volume: 81-04, Section: B.

Thesis (Ph.D.)--Cornell University, 2019.

This item must not be sold to any third party vendors.

Collecting and interpreting data is key to developing an understanding of the physical underpinnings of observable events. As such, questions of how to generate, curate and otherwise wrangle data become central as systems of interest become increasingly difficult to access experimentally and the sheer quantity of raw information explodes.The data explored in this dissertation covers a wide range of sources and methods. On the more traditional end, we explore simulation data of the two dimensional non-equilibrium random-field Ising model which we treat with a novel analytic normal form theory of the Renormalization Group. Branching out from condensed matter, we explore several machine learning and sampling methods in various contexts.The machine learning projects in particular include three lines of investigation: an unsupervised machine learning analysis of sectors of the economy extracted from stock return data, an analysis of the computational neural networks successfully applied to experimental ATLAS data in a recent Kaggle challenge, and an exploration of the geometrical underpinnings of canonical neural networks using a Jeffrey's Prior sampling of trained networks.

ISBN: 9781088392591Subjects--Topical Terms:

3343998
Computational physics.
Subjects--Index Terms:

Data

Dealings with Data: Physics, Machine Learning and Geometry.
LDR:02275nmm a2200361 4500 001 2273691
005 20201109124832.5
008 220629s2019 ||||||||||||||||| ||eng d
020 $a 9781088392591
035 $a (MiAaPQ)AAI22616491
035 $a AAI22616491
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hayden, Lorien Xanthe. $3 3551142
245 1 0 $a Dealings with Data: Physics, Machine Learning and Geometry.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 149 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
500 $a Advisor: Sethna, James Patarasp.
502 $a Thesis (Ph.D.)--Cornell University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Collecting and interpreting data is key to developing an understanding of the physical underpinnings of observable events. As such, questions of how to generate, curate and otherwise wrangle data become central as systems of interest become increasingly difficult to access experimentally and the sheer quantity of raw information explodes.The data explored in this dissertation covers a wide range of sources and methods. On the more traditional end, we explore simulation data of the two dimensional non-equilibrium random-field Ising model which we treat with a novel analytic normal form theory of the Renormalization Group. Branching out from condensed matter, we explore several machine learning and sampling methods in various contexts.The machine learning projects in particular include three lines of investigation: an unsupervised machine learning analysis of sectors of the economy extracted from stock return data, an analysis of the computational neural networks successfully applied to experimental ATLAS data in a recent Kaggle challenge, and an exploration of the geometrical underpinnings of canonical neural networks using a Jeffrey's Prior sampling of trained networks.
590 $a School code: 0058.
650 4 $a Computational physics. $3 3343998
650 4 $a Mathematics. $3 515831
650 4 $a Artificial intelligence. $3 516317
653 $a Data
653 $a Physics
653 $a Machine learning
653 $a Geometry
690 $a 0216
690 $a 0800
690 $a 0405
710 2 $a Cornell University. $b Physics. $3 3173885
773 0 $t Dissertations Abstracts International $g 81-04B.
790 $a 0058
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22616491