skip to main content
Invitado
Mi portal
Mi Cuenta
Cerrar sesión
Identificarse
This feature requires javascript
Tags
Periódicos Eletrónicos
Libros Eletrónicos
Bases de Datos
Bibliotecas de USP
Ayuda
Ayuda
Idioma:
Inglés
Castellano
Portugués (Brasil)
This feature required javascript
This feature requires javascript
Primo Search
Búsqueda General
Búsqueda General
Colección Física
Colecciones Físicas
Producción Intelectual USP
Producción USP
Search For:
Clear Search Box
Search in:
Búsqueda General
Or hit Enter to replace search target
Or select another collection:
Search in:
Búsqueda General
Búsqueda Avanzada
Búsqueda por Índices
This feature requires javascript
This feature requires javascript
Improving Pre-Trained Weights Through Meta-Heuristics Fine-Tuning
de Rosa, Gustavo H ; Roder, Mateus ; Papa, João Paulo ; Claudio F G dos Santos
arXiv.org, 2022-12
Ithaca: Cornell University Library, arXiv.org
Texto completo disponible
Citas
Citado por
Recurso en línea
Detalles
Comentarios y Etiquetas
Servicios adicionales
Veces citado
This feature requires javascript
Acciones
Agregar a Mi Portal
Eliminar de Mi Portal
Correo Electrónico
Imprimir
Enlae permanente
Cita bibliográfica
EasyBib
EndNote
RefWorks
Delicious
Exportación RIS
Exportar BibTeX
This feature requires javascript
Título:
Improving Pre-Trained Weights Through Meta-Heuristics Fine-Tuning
Autor:
de Rosa, Gustavo H
;
Roder, Mateus
;
Papa, João Paulo
;
Claudio F G dos Santos
Materias:
Algorithms
;
Computer Science - Artificial Intelligence
;
Heuristic
;
Heuristic methods
;
Image classification
;
Image reconstruction
;
Machine learning
;
Multilayer perceptrons
;
Multilayers
;
Object recognition
;
Optimization
;
Optimization techniques
;
Recurrent neural networks
Es parte de:
arXiv.org, 2022-12
Descripción:
Machine Learning algorithms have been extensively researched throughout the last decade, leading to unprecedented advances in a broad range of applications, such as image classification and reconstruction, object recognition, and text categorization. Nonetheless, most Machine Learning algorithms are trained via derivative-based optimizers, such as the Stochastic Gradient Descent, leading to possible local optimum entrapments and inhibiting them from achieving proper performances. A bio-inspired alternative to traditional optimization techniques, denoted as meta-heuristic, has received significant attention due to its simplicity and ability to avoid local optimums imprisonment. In this work, we propose to use meta-heuristic techniques to fine-tune pre-trained weights, exploring additional regions of the search space, and improving their effectiveness. The experimental evaluation comprises two classification tasks (image and text) and is assessed under four literature datasets. Experimental results show nature-inspired algorithms' capacity in exploring the neighborhood of pre-trained weights, achieving superior results than their counterpart pre-trained architectures. Additionally, a thorough analysis of distinct architectures, such as Multi-Layer Perceptron and Recurrent Neural Networks, attempts to visualize and provide more precise insights into the most critical weights to be fine-tuned in the learning process.
Editor:
Ithaca: Cornell University Library, arXiv.org
Idioma:
Inglés
Enlaces
View paper in arXiv
This feature requires javascript
This feature requires javascript
Volver a la lista de resultados
This feature requires javascript
This feature requires javascript
Buscando en bases de datos remotas, por favor espere
Buscando por
en
scope:(USP_VIDEOS),scope:("PRIMO"),scope:(USP_FISICO),scope:(USP_EREVISTAS),scope:(USP),scope:(USP_EBOOKS),scope:(USP_PRODUCAO),primo_central_multiple_fe
Mostrar lo que tiene hasta ahora
This feature requires javascript
This feature requires javascript