Advanced Machine Learning & Deep Learning
Research on advanced Machine Learning (ML) and Deep Learning (DL) has been at the forefront of artificial intelligence, paving the way for groundbreaking applications and transformative technologies. As computational power and data availability continue to grow exponentially, researchers have been able to push the boundaries of traditional machine learning techniques and delve into the complexities of deep learning architectures. These cutting-edge studies focus on improving model performance, interpretability, and generalization and addressing challenges like overfitting, non-conventional tasks and adversarial attacks. Moreover, advancements in neural network architectures, such as transformers, have revolutionized learning and computer vision tasks, enabling machines to understand any data and generate human-like text and images. As the research community delves deeper into this field, it opens up possibilities for autonomous systems, personalized medicine, smart cities, and enhanced human-computer interactions, promising a future where AI augments human capabilities and revolutionizes numerous industries.
Main current DaSCI research lines:
- Advanced Machine Learning: Advancing machine learning techniques to handle unconventional data inputs, predictive models with prior knowledge for reliable and fair decisions, emphasizing efficient Big Data processing, exploring hybridizations with soft computing, aiming to revolutionize the field and focus on AutoML for non-conventional learning challenges.
- Deep Learning in Computer Vision: Our research focuses on computer vision, exploring diffusion-based generative models for facial aging and leveraging Vision Transformers for semi-supervised learning to enhance computer vision applications in diverse industrial contexts.
- Deep Learning and Symbolic AI: Progressing in Artificial Intelligence through theoretical contributions and combining deep learning with symbolic AI, exploring graph neural networks for constraint satisfaction and social network analysis, and using neural logic machines for automatic planning, bridging symbolic and sub-symbolic AI techniques.
Related Researchers:
Letra: |
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| Name | Area | Cat. | ||
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Aguirre Molina, Eugenio | eaguirre@decdmobu9_sai.ugr.es | – | |
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Bello García, Marilyn | mbgarcia@ugrb2W8qrdv.es | – | |
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Benítez Sánchez, José Manuel | J.M.Benitez@delF.dFTvdrCOFcsai.ugr.es | – | |
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Carmona del Jesus, Cristóbal J. | ccarmona@ujsLugx9uM@aen.es | – | |
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Charte Ojeda, Francisco | fcharte@ujakNE8O_ScWwen.es | – | |
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Fernandez Olivares, Juan | faro@decsabv9zTHHi.ugr.es | – | |
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García Martínez, Carlos | cgarcia5dqgnSgG2pz@uco.es | – | |
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García Silvente, Miguel | m.garcia-silvente@decsai.ugBOjY6c_B0gr.es | – | |
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González Muñoz, Antonio | A.Gonzalez@decsxCHLjuhTai.ugr.es | Data Science and Big Data Area | PhD |
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Górriz Sáez, Juan Manuel | gorriz@u2e2JtoM5x6Ogr.es | PhD | |
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Lastra Leidinger, Miguel | mlastral@ugrwrVXQhk.es | – | |
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Luna Ariza, Jose María | jmluna3He4xP@uco.es | – | |
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Martínez del Río, Francisco | fmartin@ujaenYTFxG_a2KVC.es | – | |
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Moyano Murillo, Jose María | jmoyano@ucoGFUdKpCze.es | – | |
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Ortíz García, Andrés | aortiz@ic.5124J1Quma.es | – | |
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Pérez Godoy, María Dolores | lperez@uj5x5YzIOaen.es | – | |
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Ramírez Pérez de Inestrosa, Javier | javierrpli1QWWMB@ugr.es | – | |
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Ramírez Quesada, Aurora | aurora.ramirez@umaodi7tgh2AD.es | Data Science and Big Data Area | Colaborator |
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Rivera Rivas, Antonio Jesús | ariveraBeg6Pliy@VhE@ujaen.es | – | |
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Rodríguez Díaz, Francisco Javier | fjrodriguez@decV1YiwPLW_Mw_sai.ugr.es | – | |
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Rómero Salguero, José Raúl | jrromero@ucoeFYsXo.es | – | |