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Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.8K
Observational Learning01:12

Observational Learning

817
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
817
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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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
923
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

282
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Deep Neural Networks for Image-Based Dietary Assessment
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ランダム化ニューラルネットワークにおけるフォワード正則化を用いたインクリメンタルオンライン学習

Junda Wang, Minghui Hu, Ning Li

    IEEE transactions on pattern analysis and machine intelligence
    |January 12, 2026
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    まとめ

    ランダム化ニューラルネットワーク（Randomized NN）のためのインクリメンタルオンライン学習（IOL）フレームワークを提案し、継続学習における課題を克服します。このフレームワークは、特にフォワード正則化を用いることで、パフォーマンスを向上させ、後悔を軽減します。

    キーワード:
    インクリメンタルオンライン学習ランダム化ニューラルネットワークフォワード正則化継続学習後悔最小化

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    科学分野:

    • 人工知能
    • 機械学習
    • 深層学習

    背景:

    • ディープニューラルネットワークのオンライン学習は、更新の遅延、高コスト、破滅的な忘却といった問題に直面しています。
    • 既存の方法では、回顧的な再トレーニングが必要な場合が多く、リアルタイムの意思決定を妨げています。

    研究 の 目的:

    • ランダム化ニューラルネットワーク（Randomized NN）のための新しいインクリメンタルオンライン学習（IOL）フレームワークを提案すること。
    • オンラインシナリオにおける段階的、即時的な意思決定と継続的なパフォーマンス改善を可能にすること。

    主な方法:

    • リッジ正則化（-R）を用いたIOLおよびフォワード正則化（-F）を用いたIOLを含む、Randomized NNのためのIOLフレームワークを開発しました。
    • 再帰的な重み更新と可変学習率を用いた非定常バッチストリームでの-R/-Fのインクリメンタルアルゴリズムを導出しました。
    • 敵対的仮定の下での-R/-F学習者に対する相対累積後悔境界を理論的に導出しました。

    主要な成果:

    • -Rおよび-Fフレームワークは、回顧的な再トレーニングと破滅的な忘却を回避します。
    • -Fは、将来のラベルなしデータを利用し、-Rと比較してオンラインでの後悔を減らすことで、学習パフォーマンスを向上させました。
    • 理論的分析と経験的検証により、-Fによる優れたオンライン学習加速と後悔境界の低減が示されました。

    結論:

    • 提案されたRandomized NNのためのIOLフレームワークは、継続学習と分析に効果的です。
    • フォワード正則化（-F）は、オンライン学習シナリオ、特に長期時系列予測と継続学習において、リッジ正則化（-R）よりも大きな利点を提供します。