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関連する概念動画

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.3K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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関連する実験動画

Updated: Jan 30, 2026

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
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大規模なマルチモダルモデルのための次なるトークンの予測を備えたマルチモダル学習.

Xinlong Wang1, Yufeng Cui2, Jinsheng Wang2

  • 1Beijing Academy of Artificial Intelligence (BAAI), Beijing, China. xinlong.wang96@gmail.com.

Nature
|January 28, 2026
PubMed
まとめ
この要約は機械生成です。

Emu3は,新しいマルチモダルモデルで,テキスト,画像,ビデオタスクの次のトークン予測を使用しています. この統一されたアプローチは,複雑なアーキテクチャのない既存のモデルとマッチし,人工知能を進歩させています.

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関連する実験動画

Last Updated: Jan 30, 2026

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科学分野:

  • 人工知能 (AI) とは,人工知能 (AI) のことです.
  • 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.
  • コンピュータビジョン コンピュータビジョン

背景:

  • テキスト,画像,ビデオを統合した多式学習は,AIの重要な課題です.
  • 現在のアプローチは,拡散モデルや構成フレームワークなどの特殊なアーキテクチャに依存することが多い.
  • 次のトークンの予測には,高度な言語モデルがありますが,マルチモダルのアプリケーションは限られています.

研究 の 目的:

  • マルチモダルモデルの新しいファミリーであるEmu3を紹介します.
  • 次のトークンの予測のみを使用して,マルチモダルの学習に統一されたアプローチを実証します.
  • 多様なマルチモダルのタスクで最先端のパフォーマンスを達成するために.

主な方法:

  • Emu3のモデルは,次なるトークンの予測を用いてのみ訓練された.
  • モデルは,複数のモダリティで知覚と生成のタスクで評価されました.
  • 特定のアプリケーションには,ビデオ生成とビジョン・ランゲージ・アクション・モデリングが含まれています.

主要な成果:

  • Emu3は,タスク固有のモデルやフラッグシップシステムに匹敵するパフォーマンスを達成した.
  • このモデルは,高精度ビデオ生成能力を実証した.
  • Emu3は,交差した視覚言語生成とロボット操作のタスクを成功裏に実行しました.

結論:

  • 統一されたマルチモダルの学習は,次なるトークンの予測を通じて達成可能である.
  • Emu3は,大規模なマルチモダルAIのための堅牢な基盤を提供します.
  • このアプローチは,より一般的で統一されたマルチモダルインテリジェンスへの道を開きます.