【DL輪読会】SUMO: Unbiased Estimation of Log Marginal Probability for Latent Varia...Deep Learning JP
The document proposes a method called SUMO (Stochastically Unbiased Marginalization Objective) for estimating log marginal probabilities in latent variable models. SUMO uses a Russian roulette estimator to obtain an unbiased estimate of the log marginal likelihood. This allows SUMO to provide an objective function for variational inference that converges to the log marginal likelihood as more samples are taken, avoiding the bias issues of methods like VAEs and IWAE. The paper outlines SUMO, compares it to existing methods, and demonstrates its effectiveness on density estimation tasks.
【DL輪読会】SUMO: Unbiased Estimation of Log Marginal Probability for Latent Varia...Deep Learning JP
The document proposes a method called SUMO (Stochastically Unbiased Marginalization Objective) for estimating log marginal probabilities in latent variable models. SUMO uses a Russian roulette estimator to obtain an unbiased estimate of the log marginal likelihood. This allows SUMO to provide an objective function for variational inference that converges to the log marginal likelihood as more samples are taken, avoiding the bias issues of methods like VAEs and IWAE. The paper outlines SUMO, compares it to existing methods, and demonstrates its effectiveness on density estimation tasks.
【DL輪読会】Universal Trading for Order Execution with Oracle Policy DistillationDeep Learning JP
1. This document summarizes a research paper that proposes a reinforcement learning framework for optimal order execution. It uses an approach called Oracle Policy Distillation to distill knowledge from an optimal "teacher" policy into a "student" policy to improve sample efficiency despite noisy market data.
2. The experiments show that the proposed method can discover effective trading strategies and that Oracle Policy Distillation improves performance compared to baselines. It also finds that the student policy learns more efficient trading patterns than alternatives.
3. In conclusion, the framework can learn universal policies across different financial assets and the Oracle Policy Distillation approach enhances learning from limited market samples. Future work aims to distill policies learned from individual assets into universally applicable policies.
cvpaper.challengeにおいてECCVのOral論文をまとめた「ECCV 2020 報告」です。
ECCV2020 Oral論文 完全読破(1/2) [https://www.slideshare.net/cvpaperchallenge/eccv2020-oral-12/1]
pp. 7-10 ECCVトレンド
pp. 12-72 Looking at humans
pp. 73-132 Low level vision
pp. 133-198 Recognition & detection
pp. 199-262 Segmentation & scene interpretation and description, language
pp. 263-294 Video & action understanding
pp. 295-296 まとめ
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。論文サマリ作成・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ます。2020の目標は「トップ会議に30+本投稿」することです。
【DL輪読会】Universal Trading for Order Execution with Oracle Policy DistillationDeep Learning JP
1. This document summarizes a research paper that proposes a reinforcement learning framework for optimal order execution. It uses an approach called Oracle Policy Distillation to distill knowledge from an optimal "teacher" policy into a "student" policy to improve sample efficiency despite noisy market data.
2. The experiments show that the proposed method can discover effective trading strategies and that Oracle Policy Distillation improves performance compared to baselines. It also finds that the student policy learns more efficient trading patterns than alternatives.
3. In conclusion, the framework can learn universal policies across different financial assets and the Oracle Policy Distillation approach enhances learning from limited market samples. Future work aims to distill policies learned from individual assets into universally applicable policies.
cvpaper.challengeにおいてECCVのOral論文をまとめた「ECCV 2020 報告」です。
ECCV2020 Oral論文 完全読破(1/2) [https://www.slideshare.net/cvpaperchallenge/eccv2020-oral-12/1]
pp. 7-10 ECCVトレンド
pp. 12-72 Looking at humans
pp. 73-132 Low level vision
pp. 133-198 Recognition & detection
pp. 199-262 Segmentation & scene interpretation and description, language
pp. 263-294 Video & action understanding
pp. 295-296 まとめ
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。論文サマリ作成・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ます。2020の目標は「トップ会議に30+本投稿」することです。