DLゼミで発表した論文紹介のスライドです
World Models
David Ha (Google Brain),Jürgen Schmidhuber (NNAISENSE, Swiss AI Lab, IDSIA (USI & SUPSI))
https://arxiv.org/abs/1803.10122
The document contains contact information for Ichigaku Takigawa including their email address ichigaku.takigawa@riken.jp, personal website URL https://itakigawa.github.io/, and mentions they are working with IBISML and ATR on materials informatics and bioinformatics. It also includes a link to their page https://itakigawa.page.link/IBISML for a PDF document.
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.
The document contains contact information for Ichigaku Takigawa including their email address ichigaku.takigawa@riken.jp, personal website URL https://itakigawa.github.io/, and mentions they are working with IBISML and ATR on materials informatics and bioinformatics. It also includes a link to their page https://itakigawa.page.link/IBISML for a PDF document.
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 tuning hyperparameters using cross validation. It begins by motivating the need for model selection to choose hyperparameters that provide a good balance between model complexity and accuracy. It then discusses assessing model quality using measures like error rate from a test set. Cross validation techniques like k-fold and leave-one-out are presented as methods for estimating accuracy without using all the data for training. The document concludes by discussing strategies for implementing model selection like using grids to search hyperparameters and evaluating results.
The document contains notes from an R training session in Japan. Key points include:
- The session covered basic R functions like q(), help(), sqrt(), log(), c(), and how to create vectors and matrices.
- More advanced topics included subsetting matrices using indices, reading in data from files using functions like read.csv() and read.delim(), and creating tables and summaries of data.
- Examples and practice problems were provided using toy data and matrices to help attendees learn how to use R for data analysis.
The document discusses using factor analysis to analyze a dataset with 5 variables and 200 observations. It performs factor analysis with 2 factors using varimax rotation and promax rotation. It also examines the unrotated factor solution. The analysis finds that 2 factors explain 54.3% of the total variance in the variables.
Trading volume mapping R in recent environment Nagi Teramo
?
This document discusses visualizing trading volume data using R. It includes:
1. Loading and manipulating trading volume data from 2012 using data.table and dplyr packages to summarize the monthly total trading amount by country.
2. Creating an interactive choropleth map visualization of the monthly trading data over time using the rMaps package. Custom HTML and JavaScript are used to animate the map updating each month.
3. Providing the full codes in a GitHub repository for others to reproduce the analysis and visualization.
68. 参考文献
? Merton, R.C. (1974):On the pricing of corporate debt: The risk structure
of interest rates, Journal of Finance, 29, 449-470.
? Kealhofer, S. and Bohn, J.R. (2001): Portfolio management of default
risk. KMV working paper
? Crosbie, P.J and Bohn, J.R. (2002):Modeling default risk. KMV working
paper
? RiskMetrics Group (1997): Creditmetrics – technical document. The
benchmark for understanding credit risk
? Alexander J. McNeil, Ruediger Frey, Paul Embrechts :定量的リスク管理
-基礎概念と数理技法
? Philipp J. Sch¨onbucher :クレジット?デリバティブ―モデルと価格評価
68