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Article

Mosca, Alan and Magoulas, George D. (2018) Customised ensemble methodologies for deep learning: boosted residual networks and related approaches. Neural Computing and Applications 31 (6), pp. 1713-1731. ISSN 0941-0643.

Mosca, Alan and Magoulas, George D. (2018) Hardening against adversarial examples with the smooth gradient method. Soft Computing 22 (10), pp. 3203-3213. ISSN 1432-7643.

Book Section

Mosca, Alan and Magoulas, George D. (2017) Boosted Residual Networks. In: Boracchi, G. and Iliadis, L. and Jayne, C. and Likas, A. (eds.) Engineering Applications of Neural Networks. Communications in Computer and Information Science 744. Springer, pp. 137-148. ISBN 9783319651712.

Mosca, Alan and Magoulas, George D. (2016) Learning input features representations in deep learning. In: Angelov, P. and Gegov, A. and Jayne, C. and Shen, Q. (eds.) Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK. Advances in Intelligent Systems and Computing (AISC) 513. Berlin, Germany: Springer, pp. 433-445. ISBN 9783319465616.

Conference or Workshop Item

Athanasopoulou, Maria Eleni and Deveikyte, Justina and Mosca, Alan and Peri, Ilaria and Provetti, Alessandro (2021) A hybrid model for forecasting short-term electricity demand. In: 2nd ACM International Conference on AI in Finance, 03-05 Nov 2021, London, UK. (In Press)