國立高雄大學圖資館 |

登入

回首頁

Deep Unsupervised Models Leveraging Learning and Reasoning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep Unsupervised Models Leveraging Learning and Reasoning.
作者:	Bai, Yiwei.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2023
面頁冊數:	128 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
附註:	Advisor: Gomes, Carla P.
Contained By:	Dissertations Abstracts International84-12B.
標題:	Computer science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30418430
ISBN:	9798379711504

Deep Unsupervised Models Leveraging Learning and Reasoning.
Bai, Yiwei.

Deep Unsupervised Models Leveraging Learning and Reasoning. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 128 p.

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

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

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

Deep learning has achieved tremendous success in many fields when leveraging well-labeled large-scale datasets. However, for many challenging tasks, data are scarce, or labels are difficult to acquire. We propose different approaches to tackle such tasks. We consider two settings: 1) Deep unsupervised models for Pattern Demixing and for Traveling Salesman Problems (TSP). 1.1) We propose a general unsupervised framework (deep reasoning networks, DRNets) for demixing and inferring crystal structure. DRNets seamlessly integrate reasoning about prior scientific knowledge into neural network optimization using an interpretable latent space. As a result, DRNets require only modest amounts of (unlabeled) data. DRNets reach super-human performance for crystal-structure phase mapping, a core, long-standing challenge in materials science, solving the previously unsolved Bi–Cu–V oxide phase diagram and aiding in the discovery of solar-fuels materials. DRNets are a general framework, and we illustrate that by also demonstrating their effectiveness to demix two 9x9 complete overlapping handwritten Sudokus. We boost the performance of the state-of-the-art framework DRNets with curriculum learning with restart DRNets (CLR-DRNets) on a visual Sudoku task and a visual Sudoku demixing task. This is an extension of our early work on DRNets for unsupervised pattern demixing. Furthermore, we consider combinatorial optimization problems, where well-labeled datasets are hard to acquire. 1.2) We use zero training overhead portfolios (ZTop) to boost the performance of deep models for combinatorial optimization problems by leveraging the randomness of the optimization process. 1.3) We use unsupervised learning combined with local search to solve a large-scale Traveling Salesman Problem (TSP). Apart from the deep unsupervised models, we also study the unsupervised optimization model for the dam portfolio selection problem. 2) We have developed methods to harness AI to reduce the adverse impacts of the hydropower dam proliferation in the Amazon River basin on people and nature (e.g., river fragmentation, which affects fish migration; transportation; sediment production; increase of greenhouse gases emissions; displacement of indigenous populations, etc.). More specifically, we propose methods to efficiently approximate high-dimensional Pareto frontiers for tree-structured networks using expansion and compression methods.

ISBN: 9798379711504Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

Computational sustainability

Deep Unsupervised Models Leveraging Learning and Reasoning.
LDR:03644nmm a2200397 4500 001 655750
005 20240414211921.5
006 m o d
007 cr#unu||||||||
008 240620s2023 ||||||||||||||||| ||eng d
020 $a 9798379711504
035 $a (MiAaPQ)AAI30418430
035 $a AAI30418430
040 $a MiAaPQ $c MiAaPQ
100 1 $a Bai, Yiwei. $0 (orcid)0000-0001-7270-6684 $3 966812
245 1 0 $a Deep Unsupervised Models Leveraging Learning and Reasoning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 128 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
500 $a Advisor: Gomes, Carla P.
502 $a Thesis (Ph.D.)--Cornell University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Deep learning has achieved tremendous success in many fields when leveraging well-labeled large-scale datasets. However, for many challenging tasks, data are scarce, or labels are difficult to acquire. We propose different approaches to tackle such tasks. We consider two settings: 1) Deep unsupervised models for Pattern Demixing and for Traveling Salesman Problems (TSP). 1.1) We propose a general unsupervised framework (deep reasoning networks, DRNets) for demixing and inferring crystal structure. DRNets seamlessly integrate reasoning about prior scientific knowledge into neural network optimization using an interpretable latent space. As a result, DRNets require only modest amounts of (unlabeled) data. DRNets reach super-human performance for crystal-structure phase mapping, a core, long-standing challenge in materials science, solving the previously unsolved Bi–Cu–V oxide phase diagram and aiding in the discovery of solar-fuels materials. DRNets are a general framework, and we illustrate that by also demonstrating their effectiveness to demix two 9x9 complete overlapping handwritten Sudokus. We boost the performance of the state-of-the-art framework DRNets with curriculum learning with restart DRNets (CLR-DRNets) on a visual Sudoku task and a visual Sudoku demixing task. This is an extension of our early work on DRNets for unsupervised pattern demixing. Furthermore, we consider combinatorial optimization problems, where well-labeled datasets are hard to acquire. 1.2) We use zero training overhead portfolios (ZTop) to boost the performance of deep models for combinatorial optimization problems by leveraging the randomness of the optimization process. 1.3) We use unsupervised learning combined with local search to solve a large-scale Traveling Salesman Problem (TSP). Apart from the deep unsupervised models, we also study the unsupervised optimization model for the dam portfolio selection problem. 2) We have developed methods to harness AI to reduce the adverse impacts of the hydropower dam proliferation in the Amazon River basin on people and nature (e.g., river fragmentation, which affects fish migration; transportation; sediment production; increase of greenhouse gases emissions; displacement of indigenous populations, etc.). More specifically, we propose methods to efficiently approximate high-dimensional Pareto frontiers for tree-structured networks using expansion and compression methods.
590 $a School code: 0058.
650 4 $a Computer science. $3 199325
650 4 $a Information technology. $3 184390
653 $a Computational sustainability
653 $a Deep unsupervised learning
653 $a Reasoning
653 $a Pattern Demixing
653 $a DRNets
690 $a 0800
690 $a 0489
690 $a 0984
710 2 $a Cornell University. $b Computer Science. $3 730438
773 0 $t Dissertations Abstracts International $g 84-12B.
790 $a 0058
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30418430