博碩士論文 107423036 詳細資訊




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姓名 曾欽緹(ChinTi Tseng)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 以漸進式基因演算法實現神經網路架構搜尋最佳化
(A Progressive Genetic-based Optimization for Network Architecture Search)
相關論文
★ Enhanced Model Agnostic Meta Learning with Meta Gradient Memory
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2023-7-31以後開放)
摘要(中) 機器學習是一門從數據中由電腦自行學習得出特徵,再利用特徵對未知數
據進行預測的技術。機器學習這門技術會因所針對的目標資料集不同,而設計
出對應的模型架構,也因此所需對應專業知識、所需花費的研究時間與資源甚
多,在普遍應用的期望下有一定的門檻與瓶頸。為了加速神經網路的建構
,我們建構了一套基於演化演算法,結合深度學習技術,漸進式概念的建模演
算法,搭配經過設計的細胞結構,應用在運算資源稀缺的環境下,並在針對特
定資料集的背景下,自動搜尋出對應最優的神經網路架構。
摘要(英) No matter designing a new neural network (NN) architectures or modifying an existed model require both human expertise and intense computational resources. We propose a progressive strategy to develop models on a “meta” level which recently arose interests of experts. This meta-modeling algorithm is based on evolutionary algorithms and deep learning techniques to generate NN architectures for a given task automatically. The work we did also includes encoding a model structure into many “cells” in a continual representation. Therefore, after defining the cell structure and its topology, we find the structures for the given task cell by cell, brick by brick, and find a structure which has the highest accuracy eventually.
關鍵字(中) ★ 機器學習
★ 深度學習
★ 神經架構搜尋
★ 基因演算法
★ 機器學習自動化
關鍵字(英) ★ Machine Learning
★ Evolutionary Algorithm
★ Neural Architecture Search
★ Automated Machine Learning
★ Deep Learning
論文目次 中文摘要 i
Abstract ii
Table of contents iii
1. Introduction 1
2. Related Work 5
3. Proposed Methodology 10
3.1 Predictor Training 13
3.2 Generator Training 14
3.3 PG-NAS 15
3.4 Advantages of PG-NAS 17
4. Experiment Details 18
5. Experiment Results 20
5.1 Performance Analysis 20
5.2 Parameter Analysis 24
5.3 Predictor Analysis 28
5.4 Generator Analysis 33
6. Discussion & Future Work 36
6.1 Case Study 36
6.2 Search Efficiency 37
6.3 Future Work 38
7. Conclusion 39
References 40
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指導教授 陳以錚 周惠文(Yi-Chen Chen Huey-Wen Chou) 審核日期 2020-7-16
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