Submit Search
Upload
keynoteでslideshare
•
0 likes
•
317 views
Maruyama Tetsutaro
Follow
フォントを Noto sans CJK JP にしたら slideshare でもフォントが消えないのではないか
Read less
Read more
Design
Report
Share
Report
Share
1 of 3
Download now
Download to read offline
Recommended
Japanese version only
配列数式
配列数式
Maruyama Tetsutaro
Japanese version only
Zipf分布に従う乱数の生成方法
Zipf分布に従う乱数の生成方法
Maruyama Tetsutaro
Japanese version only. 円錐曲線を極座標で表すやり方
円錐曲線の極座標表示
円錐曲線の極座標表示
Maruyama Tetsutaro
Japaneser version only
ランダム・シャッフル
ランダム・シャッフル
Maruyama Tetsutaro
shot note で手書きの数式を取り込んで資料化
shot note で手書き資料作成
shot note で手書き資料作成
Maruyama Tetsutaro
Online Matching and Ad Allocation 輪読会用資料
Online Matching and Ad Allocaton 8章＆9章半分
Online Matching and Ad Allocaton 8章＆9章半分
Maruyama Tetsutaro
弊社論文輪読会での発表資料
ユークリッド距離以外の距離で教師無しクラスタリング
ユークリッド距離以外の距離で教師無しクラスタリング
Maruyama Tetsutaro
[Boucheron, et al. 2013] の読書会の資料です
集中不等式のすすめ [集中不等式本読み会#1]
集中不等式のすすめ [集中不等式本読み会#1]
Kentaro Minami
Recommended
Japanese version only
配列数式
配列数式
Maruyama Tetsutaro
Japanese version only
Zipf分布に従う乱数の生成方法
Zipf分布に従う乱数の生成方法
Maruyama Tetsutaro
Japanese version only. 円錐曲線を極座標で表すやり方
円錐曲線の極座標表示
円錐曲線の極座標表示
Maruyama Tetsutaro
Japaneser version only
ランダム・シャッフル
ランダム・シャッフル
Maruyama Tetsutaro
shot note で手書きの数式を取り込んで資料化
shot note で手書き資料作成
shot note で手書き資料作成
Maruyama Tetsutaro
Online Matching and Ad Allocation 輪読会用資料
Online Matching and Ad Allocaton 8章＆9章半分
Online Matching and Ad Allocaton 8章＆9章半分
Maruyama Tetsutaro
弊社論文輪読会での発表資料
ユークリッド距離以外の距離で教師無しクラスタリング
ユークリッド距離以外の距離で教師無しクラスタリング
Maruyama Tetsutaro
[Boucheron, et al. 2013] の読書会の資料です
集中不等式のすすめ [集中不等式本読み会#1]
集中不等式のすすめ [集中不等式本読み会#1]
Kentaro Minami
20140925 PFI seminar (ver. 1.0)
How to Develop Experiment-Oriented Programs
How to Develop Experiment-Oriented Programs
Kenta Oono
日本計算機統計学会第30回大会にて発表
機械学習向けプログラミング言語の使い分け - RCO の場合
機械学習向けプログラミング言語の使い分け - RCO の場合
Maruyama Tetsutaro
Preferred Seminar 2016/1/21
Stochastic Gradient MCMC
Stochastic Gradient MCMC
Kenta Oono
JapanR 2016でのLTのときの資料です。 xgboost::xgb.DMatrixを作るときのメモリ消費量を抑えるために、一旦libsvm形式のファイルを出力してから読み込む方法を使うようにしました。このとき高速にlibsvm形式でダンプするために、data.table::fwriteを魔改造したと言うお話です。
高速・省メモリにlibsvm形式で ダンプする方法を研究してみた
高速・省メモリにlibsvm形式で ダンプする方法を研究してみた
Keisuke Hosaka
This material consists of an introduction of a paper titled “Fairness in Learning: Classic and Contextual Bandits” from NIPS2016. This is presented at https://connpass.com/event/47580/.
Introduction of “Fairness in Learning: Classic and Contextual Bandits”
Introduction of “Fairness in Learning: Classic and Contextual Bandits”
Kazuto Fukuchi
NIPS2016読み会の発表資料です イベント: https://connpass.com/event/47580/ 紹介論文: http://papers.nips.cc/paper/6050-differential-privacy-without-sensitivity
Differential privacy without sensitivity [NIPS2016読み会資料]
Differential privacy without sensitivity [NIPS2016読み会資料]
Kentaro Minami
For my presentation for a reading group. I have not in any way contributed this study, which is done by the researchers named on the first slide. https://papers.nips.cc/paper/6418-interaction-networks-for-learning-about-objects-relations-and-physics
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and Physics
Ken Kuroki
NIPS2016読み会ポスター発表資料
Conditional Image Generation with PixelCNN Decoders
Conditional Image Generation with PixelCNN Decoders
suga93
論文「Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning」紹介スライドです。NIPS2016読み会@PFN(2017/1/19) https://connpass.com/event/47580/ にて。
Introduction of "TrailBlazer" algorithm
Introduction of "TrailBlazer" algorithm
Katsuki Ohto
NIPS2016論文読み会の資料です。 Material for NIPS paper reading meetup
Fast and Probvably Seedings for k-Means
Fast and Probvably Seedings for k-Means
Kimikazu Kato
NIPS2016読み会発表ポスター
InfoGAN: Interpretable Representation Learning by Information Maximizing Gen...
InfoGAN: Interpretable Representation Learning by Information Maximizing Gen...
Shuhei Yoshida
This slide was created for NIPS 2016 study meetup. IAF and other related researches are briefly explained. paper: Diederik P. Kingma et al., "Improving Variational Inference with Inverse Autoregressive Flow", 2016 https://papers.nips.cc/paper/6581-improving-variational-autoencoders-with-inverse-autoregressive-flow
Improving Variational Inference with Inverse Autoregressive Flow
Improving Variational Inference with Inverse Autoregressive Flow
Tatsuya Shirakawa
Presentation for ICDM 2016
Fractality of Massive Graphs: Scalable Analysis with Sketch-Based Box-Coverin...
Fractality of Massive Graphs: Scalable Analysis with Sketch-Based Box-Coverin...
Kenko Nakamura
NIPS読み会2016で発表したものです。
Learning to learn by gradient descent by gradient descent
Learning to learn by gradient descent by gradient descent
Hiroyuki Fukuda
This slide shows Value Iteration Network which is presented in NIPS 2016.
Value iteration networks
Value iteration networks
Fujimoto Keisuke
2014/10/30 PyData Tokyo Meetup #1
Development and Experiment of Deep Learning with Caffe and maf
Development and Experiment of Deep Learning with Caffe and maf
Kenta Oono
NIPS2016読み会発表資料 https://connpass.com/event/47580/
Safe and Efficient Off-Policy Reinforcement Learning
Safe and Efficient Off-Policy Reinforcement Learning
mooopan
Dual Learning for Machine Translation (NIPS 2016)
Dual Learning for Machine Translation (NIPS 2016)
Dual Learning for Machine Translation (NIPS 2016)
Toru Fujino
NIPS2016読み会@Preferred Networksにて、ポスター発表した際の資料です。
Matching networks for one shot learning
Matching networks for one shot learning
Kazuki Fujikawa
5分でわかるベイズ確率
5分でわかるベイズ確率
hoxo_m
put DATA on AWS S3, then fire AWS Lambda tells the S3 path to rundeck.
Lambda and rundeck
Lambda and rundeck
Maruyama Tetsutaro
Mining of Massive Datasets 3. Finding Similar Items
Mining of massive datasets chapter3
Mining of massive datasets chapter3
Maruyama Tetsutaro
More Related Content
Viewers also liked
20140925 PFI seminar (ver. 1.0)
How to Develop Experiment-Oriented Programs
How to Develop Experiment-Oriented Programs
Kenta Oono
日本計算機統計学会第30回大会にて発表
機械学習向けプログラミング言語の使い分け - RCO の場合
機械学習向けプログラミング言語の使い分け - RCO の場合
Maruyama Tetsutaro
Preferred Seminar 2016/1/21
Stochastic Gradient MCMC
Stochastic Gradient MCMC
Kenta Oono
JapanR 2016でのLTのときの資料です。 xgboost::xgb.DMatrixを作るときのメモリ消費量を抑えるために、一旦libsvm形式のファイルを出力してから読み込む方法を使うようにしました。このとき高速にlibsvm形式でダンプするために、data.table::fwriteを魔改造したと言うお話です。
高速・省メモリにlibsvm形式で ダンプする方法を研究してみた
高速・省メモリにlibsvm形式で ダンプする方法を研究してみた
Keisuke Hosaka
This material consists of an introduction of a paper titled “Fairness in Learning: Classic and Contextual Bandits” from NIPS2016. This is presented at https://connpass.com/event/47580/.
Introduction of “Fairness in Learning: Classic and Contextual Bandits”
Introduction of “Fairness in Learning: Classic and Contextual Bandits”
Kazuto Fukuchi
NIPS2016読み会の発表資料です イベント: https://connpass.com/event/47580/ 紹介論文: http://papers.nips.cc/paper/6050-differential-privacy-without-sensitivity
Differential privacy without sensitivity [NIPS2016読み会資料]
Differential privacy without sensitivity [NIPS2016読み会資料]
Kentaro Minami
For my presentation for a reading group. I have not in any way contributed this study, which is done by the researchers named on the first slide. https://papers.nips.cc/paper/6418-interaction-networks-for-learning-about-objects-relations-and-physics
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and Physics
Ken Kuroki
NIPS2016読み会ポスター発表資料
Conditional Image Generation with PixelCNN Decoders
Conditional Image Generation with PixelCNN Decoders
suga93
論文「Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning」紹介スライドです。NIPS2016読み会@PFN(2017/1/19) https://connpass.com/event/47580/ にて。
Introduction of "TrailBlazer" algorithm
Introduction of "TrailBlazer" algorithm
Katsuki Ohto
NIPS2016論文読み会の資料です。 Material for NIPS paper reading meetup
Fast and Probvably Seedings for k-Means
Fast and Probvably Seedings for k-Means
Kimikazu Kato
NIPS2016読み会発表ポスター
InfoGAN: Interpretable Representation Learning by Information Maximizing Gen...
InfoGAN: Interpretable Representation Learning by Information Maximizing Gen...
Shuhei Yoshida
This slide was created for NIPS 2016 study meetup. IAF and other related researches are briefly explained. paper: Diederik P. Kingma et al., "Improving Variational Inference with Inverse Autoregressive Flow", 2016 https://papers.nips.cc/paper/6581-improving-variational-autoencoders-with-inverse-autoregressive-flow
Improving Variational Inference with Inverse Autoregressive Flow
Improving Variational Inference with Inverse Autoregressive Flow
Tatsuya Shirakawa
Presentation for ICDM 2016
Fractality of Massive Graphs: Scalable Analysis with Sketch-Based Box-Coverin...
Fractality of Massive Graphs: Scalable Analysis with Sketch-Based Box-Coverin...
Kenko Nakamura
NIPS読み会2016で発表したものです。
Learning to learn by gradient descent by gradient descent
Learning to learn by gradient descent by gradient descent
Hiroyuki Fukuda
This slide shows Value Iteration Network which is presented in NIPS 2016.
Value iteration networks
Value iteration networks
Fujimoto Keisuke
2014/10/30 PyData Tokyo Meetup #1
Development and Experiment of Deep Learning with Caffe and maf
Development and Experiment of Deep Learning with Caffe and maf
Kenta Oono
NIPS2016読み会発表資料 https://connpass.com/event/47580/
Safe and Efficient Off-Policy Reinforcement Learning
Safe and Efficient Off-Policy Reinforcement Learning
mooopan
Dual Learning for Machine Translation (NIPS 2016)
Dual Learning for Machine Translation (NIPS 2016)
Dual Learning for Machine Translation (NIPS 2016)
Toru Fujino
NIPS2016読み会@Preferred Networksにて、ポスター発表した際の資料です。
Matching networks for one shot learning
Matching networks for one shot learning
Kazuki Fujikawa
5分でわかるベイズ確率
5分でわかるベイズ確率
hoxo_m
Viewers also liked
(20)
How to Develop Experiment-Oriented Programs
How to Develop Experiment-Oriented Programs
機械学習向けプログラミング言語の使い分け - RCO の場合
機械学習向けプログラミング言語の使い分け - RCO の場合
Stochastic Gradient MCMC
Stochastic Gradient MCMC
高速・省メモリにlibsvm形式で ダンプする方法を研究してみた
高速・省メモリにlibsvm形式で ダンプする方法を研究してみた
Introduction of “Fairness in Learning: Classic and Contextual Bandits”
Introduction of “Fairness in Learning: Classic and Contextual Bandits”
Differential privacy without sensitivity [NIPS2016読み会資料]
Differential privacy without sensitivity [NIPS2016読み会資料]
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and Physics
Conditional Image Generation with PixelCNN Decoders
Conditional Image Generation with PixelCNN Decoders
Introduction of "TrailBlazer" algorithm
Introduction of "TrailBlazer" algorithm
Fast and Probvably Seedings for k-Means
Fast and Probvably Seedings for k-Means
InfoGAN: Interpretable Representation Learning by Information Maximizing Gen...
InfoGAN: Interpretable Representation Learning by Information Maximizing Gen...
Improving Variational Inference with Inverse Autoregressive Flow
Improving Variational Inference with Inverse Autoregressive Flow
Fractality of Massive Graphs: Scalable Analysis with Sketch-Based Box-Coverin...
Fractality of Massive Graphs: Scalable Analysis with Sketch-Based Box-Coverin...
Learning to learn by gradient descent by gradient descent
Learning to learn by gradient descent by gradient descent
Value iteration networks
Value iteration networks
Development and Experiment of Deep Learning with Caffe and maf
Development and Experiment of Deep Learning with Caffe and maf
Safe and Efficient Off-Policy Reinforcement Learning
Safe and Efficient Off-Policy Reinforcement Learning
Dual Learning for Machine Translation (NIPS 2016)
Dual Learning for Machine Translation (NIPS 2016)
Matching networks for one shot learning
Matching networks for one shot learning
5分でわかるベイズ確率
5分でわかるベイズ確率
More from Maruyama Tetsutaro
put DATA on AWS S3, then fire AWS Lambda tells the S3 path to rundeck.
Lambda and rundeck
Lambda and rundeck
Maruyama Tetsutaro
Mining of Massive Datasets 3. Finding Similar Items
Mining of massive datasets chapter3
Mining of massive datasets chapter3
Maruyama Tetsutaro
DataScience, Classification, Prediction
業務に活かすデータサイエンスとは？
業務に活かすデータサイエンスとは？
Maruyama Tetsutaro
Japanese Traditional Colors. Sorry, Japanese Version only.
日本の伝統色
日本の伝統色
Maruyama Tetsutaro
Japanese version only. Gnuplotの最新パッケージの導入から日本語化、新しめの機能を使う。
Gnuplotあれこれ
Gnuplotあれこれ
Maruyama Tetsutaro
Japanese version only. Ubuntuで最新パッケージを導入する方法
Ubuntuで最新パッケージを導入
Ubuntuで最新パッケージを導入
Maruyama Tetsutaro
Japanese version only
Zshでデキるプロンプト
Zshでデキるプロンプト
Maruyama Tetsutaro
More from Maruyama Tetsutaro
(7)
Lambda and rundeck
Lambda and rundeck
Mining of massive datasets chapter3
Mining of massive datasets chapter3
業務に活かすデータサイエンスとは？
業務に活かすデータサイエンスとは？
日本の伝統色
日本の伝統色
Gnuplotあれこれ
Gnuplotあれこれ
Ubuntuで最新パッケージを導入
Ubuntuで最新パッケージを導入
Zshでデキるプロンプト
Zshでデキるプロンプト
Download now
