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

FindBook

Google Book

Amazon

博客來

Stochastic Latent Domain Approaches to the Recovery and Prediction of High Dimensional Missing Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stochastic Latent Domain Approaches to the Recovery and Prediction of High Dimensional Missing Data./
作者:	Cannella, Christopher.
面頁冊數:	1 online resource (199 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30312932click for full text (PQDT)
ISBN:	9798379569730

Stochastic Latent Domain Approaches to the Recovery and Prediction of High Dimensional Missing Data.
Cannella, Christopher.

Stochastic Latent Domain Approaches to the Recovery and Prediction of High Dimensional Missing Data. - 1 online resource (199 pages)

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

Thesis (Ph.D.)--Duke University, 2023.

Includes bibliographical references

This work presents novel techniques for approaching missing data using generative models. The main focus of these techniques is on leveraging the latent spaces of generative models, both to improve inference performance and to overcome many of the architectural challenges missing data poses for current generative models. This work includes methodologies that are broadly applicable regardless of model architecture and model specific techniques.The first half of this work is dedicated to model agnostic techniques. Here, we present our Linearized-Marginal Restricted Boltzmann Machine (LM-RBM), a method for directly approximating the conditional and marginal distributions of RBMs used to infer missing data. We also present our Semi-Empirical Ab Initio objective functions for Markov Chain Monte Carlo (MCMC) proposal optimization, which are objective functions of a restricted functional class that are fit to recover analytically known optimal proposals. These Semi-Empirical Ab Initio objective functions are shown to avoid failures exhibited by current objective functions for MCMC proposal optimization with highly expressive neural proposals and enable the more confident optimization of deep generative architectures for MCMC techniques.The second half of this work is dedicated to techniques applicable to specific generative architectures. We present Projected-Latent Markov Chain Monte Carlo (PL-MCMC), a technique for performing asymptotically exact conditional inference of missing data using normalizing flows. We evaluate the performance of PL-MCMC based on its applicability to tasks of training from and inferring missing data. We also present our Perceiver Attentional Copula for Time Series (PrACTiS), which utilizes attention with learned latent vectors to significantly improve the computational efficiency of attention based modeling in light of the additional challenges that time series data pose with respect to missing data inference.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379569730Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Generative modelsIndex Terms--Genre/Form:

542853
Electronic books.

Stochastic Latent Domain Approaches to the Recovery and Prediction of High Dimensional Missing Data.
LDR:03368nmm a2200385K 4500 001 2363500
005 20231127093422.5
006 m o d
007 cr mn ---uuuuu
008 241011s2023 xx obm 000 0 eng d
020 $a 9798379569730
035 $a (MiAaPQ)AAI30312932
035 $a AAI30312932
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Cannella, Christopher. $3 3704262
245 1 0 $a Stochastic Latent Domain Approaches to the Recovery and Prediction of High Dimensional Missing Data.
264 0 $c 2023
300 $a 1 online resource (199 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
500 $a Advisor: Tarokh, Vahid.
502 $a Thesis (Ph.D.)--Duke University, 2023.
504 $a Includes bibliographical references
520 $a This work presents novel techniques for approaching missing data using generative models. The main focus of these techniques is on leveraging the latent spaces of generative models, both to improve inference performance and to overcome many of the architectural challenges missing data poses for current generative models. This work includes methodologies that are broadly applicable regardless of model architecture and model specific techniques.The first half of this work is dedicated to model agnostic techniques. Here, we present our Linearized-Marginal Restricted Boltzmann Machine (LM-RBM), a method for directly approximating the conditional and marginal distributions of RBMs used to infer missing data. We also present our Semi-Empirical Ab Initio objective functions for Markov Chain Monte Carlo (MCMC) proposal optimization, which are objective functions of a restricted functional class that are fit to recover analytically known optimal proposals. These Semi-Empirical Ab Initio objective functions are shown to avoid failures exhibited by current objective functions for MCMC proposal optimization with highly expressive neural proposals and enable the more confident optimization of deep generative architectures for MCMC techniques.The second half of this work is dedicated to techniques applicable to specific generative architectures. We present Projected-Latent Markov Chain Monte Carlo (PL-MCMC), a technique for performing asymptotically exact conditional inference of missing data using normalizing flows. We evaluate the performance of PL-MCMC based on its applicability to tasks of training from and inferring missing data. We also present our Perceiver Attentional Copula for Time Series (PrACTiS), which utilizes attention with learned latent vectors to significantly improve the computational efficiency of attention based modeling in light of the additional challenges that time series data pose with respect to missing data inference.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 517247
650 4 $a Architectural engineering. $3 3174102
653 $a Generative models
653 $a Markov Chain Monte Carlo proposal
653 $a Missing Data
653 $a Linearized-Marginal Restricted Boltzmann Machine
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0800
690 $a 0463
690 $a 0462
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Duke University. $b Electrical and Computer Engineering. $3 1032075
773 0 $t Dissertations Abstracts International $g 84-11B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30312932 $z click for full text (PQDT)