CMA-ESサンプラーによるハイパーパラメータ最適化 at Optuna Meetup #1

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CMA-ESサンプラーによるハイパーパラメータ最適化 at Optuna Meetup #1

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東京大学松尾研究室が主催する深層強化学習サマースクールの講義で今井が使用した資料の公開版です．強化学習の基礎的な概念や理論から最新の深層強化学習アルゴリズムまで解説しています．巻末には強化学習を勉強するにあたって有用な他資料への案内も載せました．主に以下のような強化学習の概念やアルゴリズムの紹介をしています．・マルコフ決定過程・ベルマン方程式・モデルフリー強化学習・モデルベース強化学習・TD学習・Q学習・SARSA ・適格度トレース・関数近似・方策勾配法・方策勾配定理・DPG ・DDPG ・TRPO ・PPO ・SAC ・Actor-Critic ・DQN（Deep Q-Network）・経験再生・Double DQN ・Prioritized Experience Replay ・Dueling Network ・Categorical DQN ・Noisy Network ・Rainbow ・A3C ・A2C ・Gorila ・Ape-X ・R2D2 ・内発的報酬・カウントベース・擬似カウントベース・RND（Random Network Distillation）・ICM（Intrinsic Curiosity Module）・Go-Explore ・世界モデル（World Models）・MuZero ・SimPLe ・NGU（Never Give Up）・Agent57 ・AlphaGo ・AlphaGo Zero ・AlphaZero ・OpenAI Five ・AlphaStar ・マルチエージェント強化学習

強化学習の基礎と深層強化学習（東京大学松尾研究室深層強化学習サマースクール講義資料）

Shota Imai

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2016/10/12 第16回全脳アーキテクチャ勉強会＠リクルートテクノロジーズ 2016/08/01 第19回画像の認識・理解シンポジウム、チュートリアル＠浜松 2016/06/29 第3回ステアラボ人工知能セミナー＠千葉工業大学（スカイツリータウン） 2016/06/21 人工知能セミナー第7回「自然言語処理のＡＩの最新動向」＠産総研 2016/01/13 確率場と深層学習に関する第1回CRESTシンポジウム＠早稲田大学にて一部を使用。画像×言語の研究に関する日本語資料としては、現在一番網羅的だと信じています。

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SSII2020 [OS2-02] 教師あり事前学習を凌駕する「弱」教師あり事前学習 6月11日 (木) 11:00 - 12:30 メイン会場（vimeo + slido）登壇者：片岡裕雄氏（産業技術総合研究所）概要：高品質な教師ラベルを含む大規模画像データセットを学習に用いた場合は成功がほぼ確約されていると言っても差し支えないが、ラベル付けのコストは計り知れない。本発表では、SNS からの画像データ収集や弱教師によるラベル付けを含め、最小限の手間で大規模データセットを構築する方法を紹介する。800万超の人物ラベルを含む大規模データセットを人物検出のための事前学習に用いた場合、ベースラインである教師ありの事前学習手法を凌駕する検出率を実現した。

SSII2020 [OS2-02] 教師あり事前学習を凌駕する「弱」教師あり事前学習

SSII

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初めてのグラフカット

画像処理AIを用いた異常検知

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ノンパラメトリックベイズを用いた逆強化学習

【メタサーベイ】Vision and Language のトップ研究室/研究者

最近強化学習の良記事がたくさん出てきたので勉強しながらまとめた

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明治大学講演資料「機械学習と自動ハイパーパラメタ最適化」佐野正太郎

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組合せ最適化入門：線形計画から整数計画まで

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