Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

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
Retrieval01:12

Retrieval

404
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
404
Observational Learning01:12

Observational Learning

832
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...
832
Cognitive Learning01:21

Cognitive Learning

1.0K
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...
1.0K
Long-term Potentiation01:25

Long-term Potentiation

3.4K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when...
3.4K
Long-term Potentiation01:35

Long-term Potentiation

58.3K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
58.3K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Seed priming with boron nanoparticles establishes Trans-Developmental defense to Restrict Cd translocation via cell wall remodeling in Ipomoea aquatica Forsk.

Bioresource technology·2026
Same author

Gut microbiota dysbiosis and the gut-lung axis: links to asthma and its common comorbidities.

PeerJ·2026
Same author

A Temporal Knowledge Graph Generation Dataset Supervised Distantly by Large Language Models.

Scientific data·2025
Same author

TO-UGDA: target-oriented unsupervised graph domain adaptation.

Scientific reports·2024
Same author

Polar Nitride Perovskite LaWN<sub>3-δ</sub> with Orthorhombic Structure.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2023
Same author

Activation and tolerance of <i>Siegesbeckia Orientalis</i> L. rhizosphere to Cd stress.

Frontiers in plant science·2023

相关实验视频

Updated: Jan 15, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

KGMP:增强检索知识图表与多跳跃感知器.

Zhijie Yang1,2, Liyuan Weng3, Liang Zhang1,2

  • 1Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.

PloS one
|October 6, 2025
PubMed
概括

本研究介绍了知识图多节点感知器 (KGMP),这是一个新的框架,通过改进知识图中的多节点推理来增强知识基础问题答案 (KBQA). KGMP显著提高了复杂查询的性能.

更多相关视频

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.1K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

1.0K

相关实验视频

Last Updated: Jan 15, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.1K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

1.0K

科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 知识表示和推理.

背景情况:

  • 知识库问题解答 (KBQA) 旨在将自然语言查询转化为结构图查询 (GQL).
  • 现有的 KBQA 方法在 GQL 和 SQL 之间的结构差异以及多跳式推理的有限子图信息方面存在困难.
  • 当前方法中的子图可扩展性限制阻碍了多跳查询性能.

研究的目的:

  • 提出知识图多跳感知器 (KGMP),用于改进KBQA的检索生成框架.
  • 为了解决KBQA的双重挑战:GQL/SQL结构差异和多跳子图信息稀缺.
  • 通过与知识图进行深入协作,提高大型语言模型 (LLM) 的性能.

主要方法:

  • 开发了一种使用代子图扩展进行渐进推理的动态图交叉机制.
  • 设计了一个基于SparQL语法的结构互动协议,以实现高效的LLM知识图通信.
  • 实现图形结构优化技术,包括子图的重新排序和修剪,以实现紧和语义完整的子图输入.

主要成果:

  • 将KGMP作为检索模块集成到ChatKBQA框架中.
  • 在WebQSP数据集上实现了6.2%的性能改善,在CWQ数据集上达到5.3%.
  • 通过优化的子图输入,证明了增强的多跳转查询性能.

结论:

  • KGMP提供了一种新的技术范式,用于LLM和知识图之间的有效协作.
  • 拟议的框架显著提高了KBQA的性能,特别是在复杂的多跳查询中.
  • 动态图形穿越和优化的子图形表示是推动KBQA能力的关键.