ブラックボックス最適化とその応用

Jul 19, 20192 likes6,499 views

This document discusses methods for automated machine learning (AutoML) and optimization of hyperparameters. It focuses on accelerating the Nelder-Mead method for hyperparameter optimization using predictive parallel evaluation. Specifically, it proposes using a Gaussian process to model the objective function and perform predictive evaluations in parallel to reduce the number of actual function evaluations needed by the Nelder-Mead method. The results show this approach reduces evaluations by 49-63% compared to baseline methods.

Copyright © GREE, Inc. All Rights Reserved.

Copyright © GREE, Inc. All Rights Reserved.
•  
AI
•  
•
• Automated Machine Learning (AutoML)
https://y0z.github.io/about/

Copyright © GREE, Inc. All Rights Reserved.
•  
 
!  
• !
•
•
Minimize f(x)
subject to x ∈ X
f(x)

Copyright © GREE, Inc. All Rights Reserved.
•
• ! ( )
!
•
• AutoML 1 (Feurer and Hutter, 2019)
• ! !
f(x)
f(x)
f(x) x
” ”

Copyright © GREE, Inc. All Rights Reserved.
•
• !
•
•
•
•
•
•
• Grey-box Bayesian Optimization for AutoML 
https://slideslive.com/38916582/keynote-greybox-bayesian-
optimization-for-automl
f(x)

Copyright © GREE, Inc. All Rights Reserved.
•  
GP-EI SMAC TPE
•  
Population-based methods CMA-ES
•  
Nelder–Mead MADS
•
• Google Vizier (Google)
• Optuna (PFN)
• Nevergrad (Facebook)

Copyright © GREE, Inc. All Rights Reserved.
•
•
(Cohen et al., 2005; Ozaki et al., 2017)
Nelder–Mead
Nelder and Mead, 1965
CNN (Ozaki et al., 2017)

Copyright © GREE, Inc. All Rights Reserved.
Nelder–Mead
reflect, expand, inside contract, outside contract, shrink 5
reflect, expand, inside contract, outside contract shrink

Copyright © GREE, Inc. All Rights Reserved.
• Nelder–Mead
• Nelder–Mead
Nelder–Mead
Accelerating the Nelder–Mead Method with Predictive Parallel Evaluation
Yoshihiko Ozaki, Shuhei Watanabe, and Masaki Onishi 
6th ICML Workshop on Automated Machine Learning, Jun 2019.
! !f(x) ∼ GP(m(x), k(x, x′)) g(x)

Copyright © GREE, Inc. All Rights Reserved.
1.
2. Nelder–Mead
3. P
4. 2.
Nelder–Mead
! !f(x) ∼ GP(m(x), k(x, x′)) g(x)

Copyright © GREE, Inc. All Rights Reserved.
•
• 6 (Klein et
al., 2018)
• ! !
• Baseline 1 shrink ( )
• Baseline 2
•
• Baseline 1 49% 2 13%
P = 10 J = 1,2,3,4,5
Nelder–Mead
Method J Average # of eval steps Average # of evaluations
Baseline 1 - 590.27 (±141.42) 614.10 (±142.82)
Baseline 2 - 347.27 (±89.32) 3469.67 (±893.21)
Proposed 1 406.20 (±97.24) 1534.20 (±427.69)
2 314.13 (±72.26) 2307.83 (±558.02)
3 304.97 (±54.57) 2679.13 (±464.80)
4 310.60 (±67.58) 2948.20 (±642.62)
5 301.90 (±58.70) 2942.33 (±567.27)

Copyright © GREE, Inc. All Rights Reserved.
• ! !
• !
P = 10,20,30,40 J = 1,2,3,4,5
P, J
Nelder–Mead

Copyright © GREE, Inc. All Rights Reserved.
•
•
•
• 8 5 KDD AutoML Workshop
• Yoshihiko Ozaki and Masaki Onishi, 
“Practical Deep Neural Network Performance Prediction for
Hyperparameter Optimization,” 
To appear.
• https://sites.google.com/view/automl2019-workshop/

SSII2021 [OS2-01] 転移学習の基礎：異なるタスクの知識を利用するための機械学習の方法 6月10日 (木) 11:00 - 12:30 メイン会場（vimeo + sli.do）登壇者：松井孝太氏（名古屋大学）概要：転移学習とは、解きたいタスクに対して、それと異なるが似ている他のタスクからの知識（データ、特徴、モデルなど）を利用するための方法を与える機械学習のフレームワークです。深層モデルの学習方法として広く普及している事前学習モデルの利用は、この広義の転移学習の一つの実現形態とみなせます。本発表では、まず何をいつ転移するのか (what/when to transfer) といった転移学習の基本概念と定式化を説明し、具体的な転移学習の主要なアプローチとしてドメイン適応、メタ学習について解説します。

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cvpaper.challengeはコンピュータビジョン分野の今を映し、トレンドを創り出す挑戦です。論文サマリ・アイディア考案・議論・実装・論文投稿に取り組み、凡ゆる知識を共有しています。 http://xpaperchallenge.org/cv/ 本資料はViEW2021チュートリアルセッション「最新研究の始め方」のプレゼン素材です。また、xpaper.challengeの2020年末ワークショップとしてプレゼンした「研究効率化Tips」の拡張版です。本資料では3社12研究室300ページにわたるノウハウの詰め合わせです。 VIEW2021のチュートリアルセッションでは時間の制限があるため、こちらの資料から一部抜粋して発表を行うことになりますが、VIEW2021チュートリアルセッションの方にも足を運んでいただければ幸いです。 VIEW2021チュートリアルセッション：http://view.tc-iaip.org/view/2021/speaker/ts1 ViEW2021参加登録：http://view.tc-iaip.org/view/2021/registration

機械学習モデルのハイパパラメータ最適化gree_tech

The document discusses hyperparameter optimization in machine learning models. It introduces various hyperparameters that can affect model performance, and notes that as models become more complex, the number of hyperparameters increases, making manual tuning difficult. It formulates hyperparameter optimization as a black-box optimization problem to minimize validation loss and discusses challenges like high function evaluation costs and lack of gradient information.

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016/01/31(日) 第十回数学カフェ「数学史」発表資料誤って削除してしまいましたので再アップロードします。日本語のpdfがアップロードできないので、pptファイル仮アップロードします。訂正情報： p14 分母の p(B_1|A_1)の説明誤：病気でない人が陽性になる確率正：病気である人が陽性になる確率

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The document discusses best practices for updating existing projects to keep them maintainable over the long term. It recommends using the latest tools and frameworks, covering all code with tests, and using regular expressions and templating tools to help automate code migrations. Maintaining clean, well-tested code and separating concerns with aspects and dependency injection improves extensibility and reduces technical risks for legacy projects.

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This document describes adversarial inverse reinforcement learning (AIRL), which aims to learn reward functions from observed behavior. The key ideas are: 1. AIRL uses an adversarial framework where a generator policy is trained to match expert demonstrations while a discriminator tries to distinguish generator actions from expert actions. 2. The discriminator output is used as an estimate of the reward function. By maximizing the discriminator's ability to distinguish expert from generator actions, the generator learns to mimic expert behavior and implicitly infers the reward function. 3. AIRL introduces a reward shaping term to the discriminator's objective to ensure the inferred reward function satisfies the Markov property and is invariant to additive shifts. This allows stable learning of the reward function from

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The presentation on Cold Water Supply explored the fundamental principles of water distribution in buildings. It covered sources of cold water, including municipal supply, wells, and rainwater harvesting. Key components such as storage tanks, pipes, valves, and pumps were discussed for efficient water delivery. Various distribution systems, including direct and indirect supply methods, were analyzed for residential and commercial applications. The presentation emphasized water quality, pressure regulation, and contamination prevention. Common issues like pipe corrosion, leaks, and pressure drops were addressed along with maintenance strategies. Diagrams and case studies illustrated system layouts and best practices for optimal performance.

Integration of Additive Manufacturing (AM) with IoT : A Smart Manufacturing A...ASHISHDESAI85

Combining 3D printing with Internet of Things (IoT) enables the creation of smart, connected, and customizable objects that can monitor, control, and optimize their performance, potentially revolutionizing various industries. oT-enabled 3D printers can use sensors to monitor the quality of prints during the printing process. If any defects or deviations from the desired specifications are detected, the printer can adjust its parameters in real time to ensure that the final product meets the required standards.

US Patented ReGenX Generator, ReGen-X Quatum Motor EV Regenerative Accelerati...Thane Heins NOBEL PRIZE WINNING ENERGY RESEARCHER

Preface: The ReGenX Generator innovation operates with a US Patented Frequency Dependent Load Current Delay which delays the creation and storage of created Electromagnetic Field Energy around the exterior of the generator coil. The result is the created and Time Delayed Electromagnetic Field Energy performs any magnitude of Positive Electro-Mechanical Work at infinite efficiency on the generator's Rotating Magnetic Field, increasing its Kinetic Energy and increasing the Kinetic Energy of an EV or ICE Vehicle to any magnitude without requiring any Externally Supplied Input Energy. In Electricity Generation applications the ReGenX Generator innovation now allows all electricity to be generated at infinite efficiency requiring zero Input Energy, zero Input Energy Cost, while producing zero Greenhouse Gas Emissions, zero Air Pollution and zero Nuclear Waste during the Electricity Generation Phase. In Electric Motor operation the ReGen-X Quantum Motor now allows any magnitude of Work to be performed with zero Electric Input Energy. Demonstration Protocol: The demonstration protocol involves three prototypes; 1. Protytpe #1, demonstrates the ReGenX Generator's Load Current Time Delay when compared to the instantaneous Load Current Sine Wave for a Conventional Generator Coil. 2. In the Conventional Faraday Generator operation the created Electromagnetic Field Energy performs Negative Work at infinite efficiency and it reduces the Kinetic Energy of the system. 3. The Magnitude of the Negative Work / System Kinetic Energy Reduction (in Joules) is equal to the Magnitude of the created Electromagnetic Field Energy (also in Joules). 4. When the Conventional Faraday Generator is placed On-Load, Negative Work is performed and the speed of the system decreases according to Lenz's Law of Induction. 5. In order to maintain the System Speed and the Electric Power magnitude to the Loads, additional Input Power must be supplied to the Prime Mover and additional Mechanical Input Power must be supplied to the Generator's Drive Shaft. 6. For example, if 100 Watts of Electric Power is delivered to the Load by the Faraday Generator, an additional >100 Watts of Mechanical Input Power must be supplied to the Generator's Drive Shaft by the Prime Mover. 7. If 1 MW of Electric Power is delivered to the Load by the Faraday Generator, an additional >1 MW Watts of Mechanical Input Power must be supplied to the Generator's Drive Shaft by the Prime Mover. 8. Generally speaking the ratio is 2 Watts of Mechanical Input Power to every 1 Watt of Electric Output Power generated. 9. The increase in Drive Shaft Mechanical Input Power is provided by the Prime Mover and the Input Energy Source which powers the Prime Mover. 10. In the Heins ReGenX Generator operation the created and Time Delayed Electromagnetic Field Energy performs Positive Work at infinite efficiency and it increases the Kinetic Energy of the system.

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Structural QA/QC Inspection in KRP 401600 | Copper Processing Plant-3 (MOF-3)...slayshadow705

This presentation provides an in-depth analysis of structural quality control in the KRP 401600 section of the Copper Processing Plant-3 (MOF-3) in Uzbekistan. As a Structural QA/QC Inspector, I have identified critical welding defects, alignment issues, bolting problems, and joint fit-up concerns. Key topics covered: ✔ Common Structural Defects – Welding porosity, misalignment, bolting errors, and more. ✔ Root Cause Analysis – Understanding why these defects occur. ✔ Corrective & Preventive Actions – Effective solutions to improve quality. ✔ Team Responsibilities – Roles of supervisors, welders, fitters, and QC inspectors. ✔ Inspection & Quality Control Enhancements – Advanced techniques for defect detection. 📌 Applicable Standards: GOST, KMK, SNK – Ensuring compliance with international quality benchmarks. 🚀 This presentation is a must-watch for: ✅ QA/QC Inspectors, Structural Engineers, Welding Inspectors, and Project Managers in the construction & oil & gas industries. ✅ Professionals looking to improve quality control processes in large-scale industrial projects. 📢 Download & share your thoughts! Let's discuss best practices for enhancing structural integrity in industrial projects. Categories: Engineering Construction Quality Control Welding Inspection Project Management Tags: #QAQC #StructuralInspection #WeldingDefects #BoltingIssues #ConstructionQuality #Engineering #GOSTStandards #WeldingInspection #QualityControl #ProjectManagement #MOF3 #CopperProcessing #StructuralEngineering #NDT #OilAndGas

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No reputable doctor would try to conduct a basic physical exam without the help of a stethoscope. That's because the stethoscope is the best tool for gaining a basic "look" inside the key systems of the human body. Gauges perform a similar function for pumping systems, allowing technicians to "see" inside the pump without having to break anything open. Knowing what to do with the information gained takes practice and systemic thinking. This is a primer in how to do that.

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Managing MySQL in the cloud introduces a new set of challenges compared to traditional on-premises setups, from ensuring optimal performance to handling unexpected outages. In this article, we delve into covering topics such as performance tuning, cost-effective scalability, and maintaining high availability. We also explore the importance of monitoring, automation, and best practices for disaster recovery to minimize downtime.

Air pollution is contamination of the indoor or outdoor environment by any ch...dhanashree78

Air pollution is contamination of the indoor or outdoor environment by any chemical, physical or biological agent that modifies the natural characteristics of the atmosphere. Household combustion devices, motor vehicles, industrial facilities and forest fires are common sources of air pollution. Pollutants of major public health concern include particulate matter, carbon monoxide, ozone, nitrogen dioxide and sulfur dioxide. Outdoor and indoor air pollution cause respiratory and other diseases and are important sources of morbidity and mortality. WHO data show that almost all of the global population (99%) breathe air that exceeds WHO guideline limits and contains high levels of pollutants, with low- and middle-income countries suffering from the highest exposures. Air quality is closely linked to the earth’s climate and ecosystems globally. Many of the drivers of air pollution (i.e. combustion of fossil fuels) are also sources of greenhouse gas emissions. Policies to reduce air pollution, therefore, offer a win-win strategy for both climate and health, lowering the burden of disease attributable to air pollution, as well as contributing to the near- and long-term mitigation of climate change.

CS3451 INTRODUCTIONN TO OS unit ONE .pdfPonniS7

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ブラックボックス最適化とその応用

10. Copyright © GREE, Inc. All Rights Reserved. • Nelder–Mead • Nelder–Mead Nelder–Mead Accelerating the Nelder–Mead Method with Predictive Parallel Evaluation Yoshihiko Ozaki, Shuhei Watanabe, and Masaki Onishi  6th ICML Workshop on Automated Machine Learning, Jun 2019. ! !f(x) ∼ GP(m(x), k(x, x′)) g(x)

12. Copyright © GREE, Inc. All Rights Reserved. • • 6 (Klein et al., 2018) • ! ! • Baseline 1 shrink ( ) • Baseline 2 • • Baseline 1 49% 2 13% P = 10 J = 1,2,3,4,5 Nelder–Mead Method J Average # of eval steps Average # of evaluations Baseline 1 - 590.27 (±141.42) 614.10 (±142.82) Baseline 2 - 347.27 (±89.32) 3469.67 (±893.21) Proposed 1 406.20 (±97.24) 1534.20 (±427.69) 2 314.13 (±72.26) 2307.83 (±558.02) 3 304.97 (±54.57) 2679.13 (±464.80) 4 310.60 (±67.58) 2948.20 (±642.62) 5 301.90 (±58.70) 2942.33 (±567.27)

14. Copyright © GREE, Inc. All Rights Reserved. • • • • 8 5 KDD AutoML Workshop • Yoshihiko Ozaki and Masaki Onishi,  “Practical Deep Neural Network Performance Prediction for Hyperparameter Optimization,”  To appear. • https://sites.google.com/view/automl2019-workshop/

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