Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Red Neuronal Convolucional (Clasificación)× | Transformer (PLN)× | |
|---|---|---|
| Campo | Aprendizaje profundo | Aprendizaje profundo |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1998 | 2017 |
| Autor original≠ | LeCun, Y. et al. | Vaswani, A. et al. |
| Tipo≠ | Deep neural network (convolutional) | Attention-based deep neural network |
| Fuente seminal≠ | LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324. DOI ↗ | Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS. link ↗ |
| Alias | CNN (Evrişimli Sinir Ağı — Sınıflandırma), CNN classification, ConvNet, convolutional network classifier | Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | A Convolutional Neural Network (CNN) is a deep learning model, established by LeCun and colleagues in 1998, that learns local patterns directly from images and structured data to classify them. Stacks of convolutional filters discover increasingly abstract features, so manual feature engineering can be largely reduced. | The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel. |
| ScholarGateConjunto de datos ↗ |
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