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相关概念视频

Response Surface Methodology01:16

Response Surface Methodology

128
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
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The Availability Heuristic01:08

The Availability Heuristic

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
109
Associative Learning01:27

Associative Learning

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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...
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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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Classification of Systems-II01:31

Classification of Systems-II

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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,
144

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相关实验视频

Updated: Jun 29, 2025

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

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迪卡斯:一个动态的背景意识的推系统.

Zhiwen Hou1, Fanliang Bu1, Yuchen Zhou1

  • 1School of Information Network Security, People's Public Security University of China, Beijing 100038, China.

Mathematical biosciences and engineering : MBE
|March 29, 2024
PubMed
概括

本研究引入了一种新的动态上下文意识推系统,以改进实时用户兴趣建模. 该系统有效地捕获长期依赖关系和延迟交互模式,以提供更准确的动态建议.

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 动态推系统使用交互序列来模拟不断变化的用户兴趣.
  • 当前的方法经常与长期的依赖和延迟的交互模式作斗争.

研究的目的:

  • 为增强动态推提出一个动态上下文意识的推系统.
  • 改进长期依赖的建模和提取相关的延迟交互模式.

主要方法:

  • 使用了一个动态图形,将最近的交互作为动态上下文的静态嵌入.
  • 采用一个门式多层感知子编码器来实现长期依赖结构.
  • 实施了注意力聚合网络,使用双向注意力权重来提取延迟的模式.
  • 引入了一种双向共弦相似性损失函数,用于嵌入的联合优化.

主要成果:

  • 拟议的模型表现出与最先进的基线相比的持续改进.
  • 对LastFM和全球恐怖主义数据库数据集进行了实验.
  • 该系统有效地捕获了长期的依赖结构和延迟的交互模式.

结论:

  • 动态上下文意识推系统在动态推方面取得了重大进展.
关键词:
具有背景意识的推建议动态图的动态图是指一个动态图.推系统是指推系统.

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  • 该模型能够捕捉复杂的时间动态,从而提高了推准确度.
  • 该方法为动态推系统的未来研究提供了坚实的框架.