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相关概念视频

Force Classification01:22

Force Classification

1.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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Labeling Emotion01:20

Labeling Emotion

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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
188
Classification of Systems-II01:31

Classification of Systems-II

183
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
183
Aggregates Classification01:29

Aggregates Classification

350
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Updated: Jul 26, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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描述增强标签嵌入对比学习用于文本分类.

Kun Zhang, Le Wu, Guangyi Lv

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    此摘要是机器生成的。

    本研究引入了通过更好地利用标签信息来改进文本分类的新方法. 新的网络,关系关系 (RoR) 和描述增强标签嵌入 (DELE),利用自我监督学习和外部知识来提高绩效.

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    科学领域:

    • 自然语言处理自然语言处理.
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 文本分类在NLP中至关重要,深度学习和预训练语言模型 (PLM) 显示出前景.
    • 现有的方法往往忽略了标签中的丰富的语义信息,将它们视为简单的单一热向量或使用基本嵌入.

    研究的目的:

    • 开发先进的文本分类方法,有效地利用标签语义.
    • 提出新的自我监督学习任务和网络架构,以改善标签的使用.

    主要方法:

    • 引入了自我监督的关系关系 (RoR) 分类任务,以利用标签信息.
    • 提出了RoR-Net,将文本和RoR分类与标签分析的三重损失集成在一起.
    • 开发了描述增强标签嵌入 (DELE) 网络,将外部知识 (WordNet) 纳入更丰富的标签表示.
    • 实现了与对比学习 (CL) 的相互交互模块,用于在细粒度描述中减轻噪声.

    主要成果:

    • 在各种任务中,RoR-Net在文本分类性能方面取得了显著的改善.
    • 通过描述增强嵌入,DELE通过有效利用标签信息进一步提高了性能.
    • 拟议的方法显示了纳入标签语义和外部知识的好处.

    结论:

    • 新的RoR和DELE网络提供了通过深入整合标签语义来改进文本分类的有效策略.
    • 自主监督学习和外部知识整合是推进NLP任务的强大工具.
    • 发布的代码有助于在该领域进行进一步的研究.