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関連する概念動画

Associative Learning01:27

Associative Learning

1.2K
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...
1.2K
Observational Learning01:12

Observational Learning

782
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...
782
Purposive Learning01:22

Purposive Learning

411
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
411
Cognitive Learning01:21

Cognitive Learning

960
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
960
Stereotype Content Model02:16

Stereotype Content Model

15.3K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.3K
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

400
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
400

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関連する実験動画

Updated: Jan 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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HiCL:教師なし文埋め込みの階層的対照学習

Zhuofeng Wu1, Chaowei Xiao2, Vg Vinod Vydiswaran1

  • 1University of Michigan, Ann Arbor.

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing
|December 22, 2025
PubMed
まとめ

本研究では、局所的および全体的な関係の両方を考慮してテキスト表現を改善する階層的対照学習フレームワークであるHiCLを紹介します。HiCLは、意味的テキスト類似性(STS)タスクのトレーニング効率と有効性を向上させます。

科学分野:

  • 自然言語処理
  • 機械学習
  • 人工知能

背景:

  • 従来のシーケンスエンコーディング法は、しばしば局所的なテキストの特徴を見落としており、短いテキストへの一般化を妨げている。
  • 既存のアプローチは、トレーニング効率と表現学習の有効性のバランスをとる上で課題に直面している。

研究 の 目的:

  • HiCL、新しい階層的対照学習フレームワークを提案すること。
  • テキスト表現学習におけるトレーニング効率と有効性を強化すること。
  • 意味的テキスト類似性(STS)タスクのパフォーマンスを向上させること。

主な方法:

  • HiCLは階層的アプローチを採用し、局所的なセグメントレベルと全体的なシーケンスレベルの両方でテキストを処理する。
  • セグメントとシーケンスの表現の両方に対して対照学習を利用する。
  • 短いセグメントを最初に処理し、次にそれらを集約することで効率的なエンコーディングを実現し、トランスフォーマーの二次的複雑性に対処する。

主要な成果:

  • HiCLは、7つのSTSタスクでSNCSEモデルのパフォーマンスを大幅に向上させる。
  • BERTlargeで+0.2%、RoBERTalargeで+0.44%の平均改善が観察された。
キーワード:
階層的対照学習文埋め込みテキスト表現意味的テキスト類似性自然言語処理

さらに関連する動画

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

548

関連する実験動画

Last Updated: Jan 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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Published on: December 6, 2024

983
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

548
  • このフレームワークは、従来のメソッドと比較して優れた有効性と効率を示す。
  • 結論:

    • HiCLは、テキスト表現学習に効果的かつ効率的なアプローチを提供する。
    • 階層的対照学習戦略は、局所的および全体的なテキスト関係の両方をうまくモデル化する。
    • このフレームワークは、意味的テキスト類似性研究の進歩のための強力な基盤を提供する。