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.
ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
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.
ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
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.
This document discusses the development of libkonn, a Haskell library for accessing Twitter's APIs. It uses Haskell and ByteString to process JSON from the Twitter stream and REST APIs. Multiple streamers can run concurrently to gather tweets and store them in MongoDB. The library includes a tweet extractor that analyzes tweet text and metadata to identify replies, retweets, and mentions. It also provides a demo web interface built with Snap and Heist that visualizes tweet relationships as a graph.
Template Haskell allows code to be generated at compile time by splicing quasi quotations into the abstract syntax tree. It works by running Haskell code that constructs syntax expressions and splicing the results into the code being compiled. This allows features like generating boilerplate code, domain-specific languages, and compile-time metaprogramming in Haskell.
IoT Devices Compliant with JC-STAR Using Linux as a Container OSTomohiro Saneyoshi
Security requirements for IoT devices are becoming more defined, as seen with the EU Cyber Resilience Act and Japan’s JC-STAR.
It's common for IoT devices to run Linux as their operating system. However, adopting general-purpose Linux distributions like Ubuntu or Debian, or Yocto-based Linux, presents certain difficulties. This article outlines those difficulties.
It also, it highlights the security benefits of using a Linux-based container OS and explains how to adopt it with JC-STAR, using the "Armadillo Base OS" as an example.
Feb.25.2025@JAWS-UG IoT
18. 例
a1 = X + Y
a2 = 1
XY − 1
Y2
− 1
X2
Y + XY2
+ Y2
X2
Y − X
XY2
+ X + Y2
XY2
− Y
X + Y2
− Y
X
Y2
− Y
Y2
− 1
Y + 1 Y
1
0
1
r = X + Y + 1
X2
Y + XY2
+ Y2
= (XY − 1) (X+Y) + (Y2
− 1) ⋅1 + X + Y + 1
39. 参考文献
1. D.A. Cox, J. Little and D. O'Shea. Ideals,Varieties, and Algorithms. Springer-Verlag.
• 邦訳:『グレブナ基底と代数多様体入門 上・下』丸善出版
2. D.A. Cox, J. Little and D. O'Shea. Using Algebraic Geometry. Springer-Verlag.
• 邦訳:『グレブナー基底 1・2』丸善出版
3. J. Gathen and Gerhard. Modern Computer Algebra. Cambridge University Press.
• 邦訳:『コンピュータ代数ハンドブック』朝倉書店
4. JST CREST 日比チーム. 『グレブナー道場』. 共立出版.
5. Press,Teukolsky,Vetterling and Flannery. Numerical Recipes. Cambridge University Press.
6. 日比孝之 et al.“グレブナー基底の新天地”. In: 数学セミナー 2012 年 2 月号. 日本
評論社, 2012, pp. 7–52.