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関連する概念動画

Associative Learning01:27

Associative Learning

572
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...
572
Observational Learning01:12

Observational Learning

311
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...
311
Purposive Learning01:22

Purposive Learning

206
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...
206
Cognitive Learning01:21

Cognitive Learning

517
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...
517
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

149
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
149
Machines: Problem Solving II01:30

Machines: Problem Solving II

367
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
367

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DPA-2:マルチタスクの学習者としての大きな原子模型

Duo Zhang1,2,3, Xinzijian Liu1,2, Xiangyu Zhang4,5

  • 1AI for Science Institute, Beijing 100080, P. R. China.

npj computational materials
|August 25, 2025
PubMed
まとめ
この要約は機械生成です。

大型原子模型 (LAM) を用いた原子モデルの新しい枠組みを導入します. これらのAIモデルは 様々な分野に備えて 訓練されていて 多様なタスクに効率的に調整され 分子や材料のシミュレーションを 加速させることができます

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Last Updated: Sep 10, 2025

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科学分野:

  • コンピューター化学と材料科学
  • 科学的なモデリングにおける人工知能

背景:

  • 人工知能 (AI) は原子モデリング,シミュレーション,設計に革命を起こしています.
  • 人工知能が駆動する 潜在エネルギーモデルは 精密な大規模シミュレーションを可能にします
  • 現在のモデルの生成は,この分野での広範なAIアプリケーションのボトルネックです.

研究 の 目的:

  • 効率的で汎用的な分子モデリングのためのモデル中心の生態系を提案する.
  • 大型原子模型 (LAM) のプロトタイプとしてDPA-2アーキテクチャを導入する.

主な方法:

  • 大型原子模型 (LAM) として DPA-2 アーキテクチャを開発した.
  • マルチタスクのアプローチを用いて様々な化学および材料システムについて,事前に訓練されたDPA-2.
  • DPA-2の汎用性を評価した.

主要な成果:

  • DPA-2は従来のシングルタスクの予備訓練と比較して優れた汎用性を示した.
  • 提案されたモデル中心のアプローチは,多様なアプリケーションのためのモデル生成を合理化します.
  • 大規模で長期にわたるシミュレーションで高い精度を達成した.

結論:

  • DPA-2アーキテクチャとモデル中心のエコシステムは,分子モデリングのための新しい枠組みを提供します.
  • このアプローチは,材料と分子シミュレーションにおける AI の開発と応用を加速します.
  • 特定の科学的なタスクのために,事前に訓練されたモデルの効率的な微調整と蒸留を可能にします.