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Introduction to Learning

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

Vector Algebra: Graphical Method

Vector Algebra: Graphical Method

Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...

Observational Learning

Observational Learning

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...

Graphs of Functions

Graphs of Functions

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...

Automatic Processing and Automatic Social Behavior

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

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相关实验视频

Updated: Jan 17, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

自动SGRL:自动监督图形表示学习的自动化框架构建.

Yu Xie¹, Yu Chang¹, Ming Li²

¹Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, China.

Neural networks : the official journal of the International Neural Network Society

|September 24, 2025

概括

此摘要是机器生成的。

本研究介绍了AutoSGRL,这是一种用于自动构建自我监督图形表示学习框架的新方法. AutoSGRL使用遗传算法优化图形对比学习,优于手工设计.

关键词:

自动机器学习自动化机器学习图形表示学习学习学习图形表示.自主监督的对比学习学习.

更多相关视频

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

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

Published on: June 13, 2025

相关实验视频

Last Updated: Jan 17, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

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

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

Published on: June 13, 2025

科学领域:

人工智能的人工智能
机器学习机器学习
图形表示学习学习学习图形表示学习

背景情况:

自动机器学习 (AutoML) 正在推动人工智能发展.
目前的图形神经架构搜索主要集中在监督或半监督学习上.
在自主监督图形表示学习的自动化方法中存在差距.

研究的目的:

提出AutoSGRL,这是自动构建灵活自主监督图形表示学习框架的第一个方法.
为自主监督图形表示学习建立一个全面的搜索空间,包括数据增强,代理任务和超参数.
开发一个自动化的搜索引擎来优化这些框架.

主要方法:

开发了AutoSGRL,这是一个利用现有的自我监督图形对比学习方法的框架.
定义了一个包含数据增强,代理任务和图形对比学习的超参数的搜索空间.
实现了一个基于遗传算法的搜索引擎,模拟生物进化 (选择,交叉,突变) 以代优化框架.

主要成果:

AutoSGRL成功构建了自我监督的图形表示学习框架.
拟议的方法实现了与最先进的手工方法相比或更好的性能.
对手动自我监督方法和半监督图形神经架构搜索都证明了有效性.

结论:

自动SGRL代表了自动图表表示学习的重大进步.
该方法提供了一种灵活和有效的方法来构建自主监督的图形学习框架.
AutoSGRL为更容易获得和更强大的图形表示学习解决方案铺平了道路.