Publications

My Peer-Reviewed articles

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Last updated on

24.Jul.2024

List of my Peer-Reviewed publications:

Article

  1. Condensed-gradient boosting

    S. Emami and G. Martínez-Muñoz, “Condensed Gradient Boosting”, in International Journal of Machine Learning and Cybernetics, accepted and awaiting publication. DOI: 10.1007/s13042-024-02279-0. 2023 JCR: 3.1 Impact Factor, Q2 in Computer Science, Artificial Intelligence.

  2. Deep Learning for Multi-Output Regression using Gradient Boosting

    S. Emami and G. Martínez-Muñoz, “Deep Learning for Multi-Output Regression using Gradient Boosting”, in IEEE Access, vol. 12, pp. 17760-17772, 2024, DOI: 10.1109/ACCESS.2024.3359115. 2023 JCR: 3.4 Impact Factor, Q2 in Computer Science and Information Systems.

  3. A Gradient Boosting Approach for Training Convolutional and Deep Neural Networks

    • S. Emami and G. Martínez-Muñoz, “A Gradient Boosting Approach for Training Convolutional and Deep Neural Networks”, in IEEE Open Journal of Signal Processing, vol. 4, pp. 313-321, 2023, DOI: 10.1109/OJSP.2023.3279011. 2023 JCR: 2.9 Impact Factor, Q2 in Engineering, Electrical, and Electronic.

  4. Sequential Training of Neural Networks with Gradient Boosting

    • S. Emami and G. Martínez-Muñoz, "Sequential Training of Neural Networks with Gradient Boosting", in IEEE Access, vol. 11, pp. 42738-42750, 2023, DOI: 10.1109/ACCESS.2023.3271515. 2023 JCR: 3.4 Impact Factor, Q2 in Computer Science and Information Systems.

Chapter

  1. Multi-Task Gradient Boosting

    • S. Emami, C. Ruiz Pastor, G. Martínez-Muñoz, “Multi-Task Gradient Boosting”, In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-031-40725-3_9.

Conference Papers

  1. Multioutput Regression Neural Network Training via Gradient Boosting

    • S. Emami, and G. Martínez-Muñoz, “Multioutput Regression Neural Network Training via Gradient Boosting”, In 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges, Belgium, October 5-7, 2022, (pp. 95-100). DOI: 10.14428/esann/2022.ES2022-95.