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Videos de Conceptos Relacionados

Natural and Artificial Concepts01:24

Natural and Artificial Concepts

264
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
264
Concepts and Prototypes01:24

Concepts and Prototypes

220
The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
220
Stereotype Content Model02:16

Stereotype Content Model

14.9K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.9K
Schemata01:17

Schemata

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A schema is a mental construct that organizes related concepts, allowing the brain to process information efficiently. Upon activation, schemata facilitate assumptions about people or objects.
Two types of schemata are:
142
Data: Types and Distribution01:19

Data: Types and Distribution

838
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
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Language and Cognition01:27

Language and Cognition

438
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Video Experimental Relacionado

Updated: Sep 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Incorporaciones de cajas para extender las ontologías: un enfoque basado en datos e interpretable

Adel Memariani1, Martin Glauer2, Simon Flügel3

  • 1Data Science Group (DICE), Heinz Nixdorf Institute, Paderborn University, Warburger Str. 100, 33098, Paderborn, North Rhine-Westphalia, Germany. adel.memariani@uni-paderborn.de.

Journal of cheminformatics
|September 2, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce un nuevo método para el aprendizaje profundo interpretable en la clasificación de etiquetas múltiples mediante el uso de incrustaciones en forma de caja para representar relaciones jerárquicas. El enfoque logra un rendimiento de vanguardia al tiempo que garantiza la coherencia con la conceptualización ontológica.

Palabras clave:
Incorporación de la cajaEl ChEBIClasificaciónOntología

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Área de la Ciencia:

  • Inteligencia artificial
  • Informática química y sus aplicaciones
  • La bioinformática

Sus antecedentes:

  • Los modelos de aprendizaje profundo carecen de transparencia, lo que dificulta la extracción de conocimiento simbólico.
  • La IA interpretable es crucial para comprender las salidas de modelos complejos.
  • Las tareas de clasificación de etiquetas múltiples a menudo implican estructuras de etiquetas jerárquicas inherentes.

Objetivo del estudio:

  • Desarrollar un método para obtener conocimiento simbólico de modelos de aprendizaje profundo.
  • Hacer cumplir una estructura taxonómica en las salidas del modelo para una mayor interpretabilidad.
  • Representar las relaciones lógicas implícitas en conjuntos de datos de etiquetas múltiples utilizando incrustaciones geométricas.

Principales métodos:

  • Utilizó incrustaciones en forma de caja de clases de ontología en el espacio vectorial.
  • Impuso una estructura taxonómica en las salidas del modelo durante la formación.
  • Evaluación del rendimiento del modelo mediante la aproximación de las relaciones de subclase en la ontología ChEBI.

Principales resultados:

  • El modelo captura con éxito las relaciones jerárquicas implícitas entre las etiquetas.
  • Aseguró la coherencia con la conceptualización ontológica subyacente.
  • Se ha logrado un rendimiento de vanguardia en tareas de clasificación de etiquetas múltiples.

Conclusiones:

  • El enfoque propuesto permite obtener resultados interpretables en la clasificación química.
  • La representación geométrica de las moléculas y las clases facilita la comprensión de las relaciones lógicas.
  • Las jerarquías implícitas se aprenden sin una taxonomía explícita durante el entrenamiento.