Adapting Resilient Propagation for Deep Learning. - Alan Mosca; George D. Magoulas (CoRR, 2015)

Deep Incremental Boosting. - Alan Mosca; George D. Magoulas (GCAI, 2016)

Learning Input Features Representations in Deep Learning. - Alan Mosca; George D. Magoulas (UKCI, 2016)

Boosted Residual Networks. - Alan Mosca; George D. Magoulas (EANN, 2017)

Training convolutional networks with weight-wise adaptive learning rates. - Alan Mosca; George D. Magoulas (ESANN, 2017)

Hardening against adversarial examples with the smooth gradient method. - Alan Mosca; George D. Magoulas (Soft Comput., 2018)

Customised ensemble methodologies for deep learning: Boosted Residual Networks and related approaches. - Alan Mosca; George D. Magoulas (Neural Comput. Appl., 2019)

How to Calibrate your Neural Network Classifier: Getting True Probabilities from a Classification Model. - Natalia Culakova; Dan Murphy; Joao Gante; Carlos Ledezma; Vahan Hovhannisyan; Alan Mosca (KDD, 2020)

A hybrid model for forecasting short-term electricity demand. - Maria Eleni Athanasopoulou; Justina Deveikyte; Alan Mosca; Ilaria Peri; Alessandro Provetti (ICAIF, 2021)

On Forecasting Project Activity Durations with Neural Networks. - Peter Zachares; Vahan Hovhannisyan; Carlos Ledezma; Joao Gante; Alan Mosca (EANN, 2022)

Form follows Function: Text-to-Text Conditional Graph Generation based on Functional Requirements. - Peter A. Zachares; Vahan Hovhannisyan; Alan Mosca; Yarin Gal (CoRR, 2023)

Tweedie Moment Projected Diffusions for Inverse Problems. - Benjamin Boys; Mark Girolami; Jakiw Pidstrigach; Sebastian Reich; Alan Mosca; Ömer Deniz Akyildiz (Trans. Mach. Learn. Res., 2024)