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相关概念视频

The Representativeness Heuristic02:13

The Representativeness Heuristic

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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
203
Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
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Deductive Reasoning01:16

Deductive Reasoning

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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
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Inductive Reasoning00:59

Inductive Reasoning

60.4K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
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Language and Cognition01:27

Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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相关实验视频

Updated: Jun 26, 2025

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

Published on: December 6, 2024

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知识图和预训练的语言模型 增强的代表性学习用于对话性推系统.

Zhangchi Qiu, Ye Tao, Shirui Pan

    IEEE transactions on neural networks and learning systems
    |May 14, 2024
    PubMed
    概括

    本研究引入了知识增强实体表示学习 (KERL) 框架,以改进对话推系统 (CRS). 凯尔利用知识图和语言模型来更好地理解实体,在建议和响应生成方面取得最先进的结果.

    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.
    • 推系统是一个推系统.

    背景情况:

    • 对话推系统 (CRS) 使用对话历史记录来推断用户偏好.
    • 现有的CRS通常依赖于外部知识图 (KG),但忽视了内部实体信息.
    • 有限的背景和背景知识阻碍了当前CRS的性能.

    研究的目的:

    • 引入一个新的框架,即知识增强实体表示学习 (KERL),用于改进CRS.
    • 通过整合KG和预训练语言模型 (PLM) 来增强对实体的语义理解.
    • 在对话环境中提高建议准确性和响应生成质量.

    主要方法:

    • 开发了KERL框架,将KG和PLM集成为实体代表.
    • 编码的实体描述使用PLM和强化表示与KG信息.
    • 利用位置编码来捕捉对话中的实体的时间动态.
    • 构建了维基电影知识图 (WikiMKG) 用于经验评估.

    主要成果:

    • 在推任务中,KERL取得了最先进的绩效.
    • 在响应生成任务中,KERL表现出卓越的结果.
    • 该框架有效地融合了实体和上下文表示,以提供增强的建议.

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    The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
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    The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

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    Published on: December 6, 2024

    547
    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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    The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
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    The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

    Published on: February 19, 2018

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    结论:

    • 在KERL框架显著改善对话推系统.
    • 整合内部实体信息与外部知识来源对于CRS至关重要.
    • 拟议的方法为推和对话生成提供了一个强大的方法.