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相关概念视频

Observational Learning01:12

Observational Learning

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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...
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Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

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An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
185
Graphs of Functions01:30

Graphs of Functions

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Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
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Schemas01:42

Schemas

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A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

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The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
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Storage01:23

Storage

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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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ScaDyG:大规模动态图形学习的新范式

Xiang Wu, Xunkai Li, Rong-Hua Li

    IEEE transactions on neural networks and learning systems
    |January 12, 2026
    PubMed
    概括

    在ScaDyG中,通过重构拓和使用时间编码,为动态图 (DGs) 引入了一个可扩展的学习范式. 这种方法提高了下游任务的效率和性能,解决了动态图形神经网络 (DGNN) 的可扩展性问题.

    科学领域:

    • 机器学习 机器学习
    • 图形神经网络的神经网络
    • 动态系统 动态系统

    背景情况:

    • 动态图 (DGs) 模拟时间演变的关系,对于许多现实应用来说至关重要.
    • 现有的动态图形神经网络 (DGNNs) 由于历史数据的增长而面临可扩展性挑战.
    • 工业应用需要对下游任务进行高效的GD编码.

    研究的目的:

    • 提出ScaDyG,一个新的动态图的时间意识可扩展的学习范式.
    • 解决传统 DGNN 的可扩展性限制.
    • 提高GD编码下游任务的效率和性能.

    主要方法:

    • 时间感知拓重构 (TTR):将历史交互细分为时间步骤,以实现无重量,时间感知的传播.
    • 动态时间编码 (DTE):使用指数函数集成精细的时间编码.
    • 超级网络驱动的消息聚合:使用超级网络来实现节点表示的适应性时间融合.

    主要成果:

    • 在12个数据集上,ScaDyG在12个数据集上展示了与最先进的 (SOTA) 方法相比或更高的性能.
    • 在节点级和链接级下游任务中取得了强的结果.
    • 与现有方法相比,展示了更少的可学习参数和更高的计算效率.

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    结论:

    • ScaDyG为学习动态图形提供了有效和高效的解决方案.
    • 提出的方法成功地解决了DGNN中的可扩展性问题.
    • 该方法为现实世界GD应用提供了一个有希望的方向.