cvpaper.challenge の メタサーベイ発表スライドです。 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。 http://xpaperchallenge.org/cv/Read less
![【メタサーベイ】基盤モデル / Foundation Models](https://cdn-ak-scissors.b.st-hatena.com/image/square/a5181e8e70e6e112e99cadc7e3b89e05bf86f961/height=288;version=1;width=512/https%3A%2F%2Fcdn.slidesharecdn.com%2Fss_thumbnails%2Ffoundation-models-220818062424-82e259ce-thumbnail.jpg%3Fwidth%3D640%26height%3D640%26fit%3Dbounds)
1. The document discusses various statistical and neural network-based models for representing words and modeling semantics, including LSI, PLSI, LDA, word2vec, and neural network language models. 2. These models represent words based on their distributional properties and contexts using techniques like matrix factorization, probabilistic modeling, and neural networks to learn vector representatio
The document discusses control as inference in Markov decision processes (MDPs) and partially observable MDPs (POMDPs). It introduces optimality variables that represent whether a state-action pair is optimal or not. It formulates the optimal action-value function Q* and optimal value function V* in terms of these optimality variables and the reward and transition distributions. Q* is defined as t
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