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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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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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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...
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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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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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Introduction to Learning01:18

Introduction to Learning

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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...
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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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FA-GCL: Método de aprendizaje por contraste de gráficos aumentados por características

Long Xu1, Honghui Chen1

  • 1National Key Laboratory of Information Systems Engineering, Changsha, 410000, China.

Neural networks : the official journal of the International Neural Network Society
|September 5, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce el Aprendizaje Contrastivo de Gráficos basado en el Aumento de Características (FA-GCL) para mejorar las representaciones gráficas. FA-GCL mejora la precisión y la robustez mediante el uso de dropout dinámico y la descomposición de valor singular para el aumento de características, superando a los métodos existentes.

Palabras clave:
Desaparición dinámicaAumento de las característicasAprendizaje por contrasteDescomposición del valor singular

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Área de la Ciencia:

  • Aprendizaje de la Representación Gráfica
  • Aprendizaje automático
  • Ciencia de los datos

Sus antecedentes:

  • Los métodos de aprendizaje por contraste de gráficos existentes a menudo se basan en atributos completos de nodos o información estructural.
  • Los atributos de nodo incompletos y los falsos positivos de la mejora de la estructura dificultan el rendimiento en los datos de gráficos del mundo real.
  • Se necesitan técnicas robustas de aprendizaje de representación gráfica que sean menos sensibles a la integridad de los datos.

Objetivo del estudio:

  • Proponer un nuevo método de aprendizaje por contraste de gráficos basado en el aumento de características (FA-GCL).
  • Mejorar la precisión y robustez de las representaciones gráficas.
  • Abordar las limitaciones de los métodos existentes en el manejo de atributos de nodos incompletos y ruido estructural.

Principales métodos:

  • Emplea una técnica de aumento de características basada en abandono dinámico con una función de onda triangular para tasas de abandono adaptativas.
  • Introduce dos métodos de aumento de características basados en la descomposición de valor singular (SVD): el SVD completo y el SVD de proyección aleatoria.
  • Los métodos SVD agregan ruido controlado a valores singulares y reconstruyen características para muestras aumentadas de alta calidad, con SVD aleatorio que ofrece complejidad lineal.

Principales resultados:

  • FA-GCL demuestra un rendimiento superior consistente en doce conjuntos de datos de gráficos.
  • El método supera significativamente los enfoques de línea de base en las tareas de clasificación de nodos, agrupación de nodos y clasificación de gráficos.
  • El aumento de características resulta eficaz para mejorar la calidad y la robustez de las representaciones gráficas aprendidas.

Conclusiones:

  • FA-GCL ofrece un enfoque robusto y efectivo para el aprendizaje de la representación de gráficos, particularmente cuando los atributos de los nodos están incompletos.
  • Las estrategias de aumento de características propuestas mejoran el rendimiento y la generalización del modelo.
  • Este trabajo avanza en el aprendizaje por contraste de gráficos mediante la introducción de técnicas de aumento de datos flexibles y potentes.