| Mixture Density Networks (MDN) for distribution and uncertainty estimation A Brando Master’s thesis, Universitat Politecnica de Catalunya, 2017 | 51* | 2017 |
| Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians A Brando, JA Rodriguez, J Vitrià, AR Muñoz Advances in Neural Information Processing Systems, 8838-8848, 2019 | 25 | 2019 |
| Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series A Brando, JA Rodríguez-Serrano, M Ciprian, R Maestre, J Vitrià Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018 | 23 | 2018 |
| Deep Non-Crossing Quantiles through the Partial Derivative A Brando, BS Center, J Rodriguez-Serrano, J Vitria International Conference on Artificial Intelligence and Statistics, 7902-7914, 2022 | 13 | 2022 |
| Uncertainty Estimation for Black-Box Classification Models: A Use Case for Sentiment Analysis J Mena, A Brando, O Pujol, J Vitrià Iberian Conference on Pattern Recognition and Image Analysis, 29-40, 2019 | 11 | 2019 |
| Building Uncertainty Models on Top of Black-Box Predictive APIs A Brando, D Torres, JA Rodríguez-Serrano, J Vitrià IEEE Access 8, 121344-121356, 2020 | 7 | 2020 |
| Using Quantile Regression in Neural Networks for Contention Prediction in Multicore Processors A Brando, I Serra, E Mezzetti, J Abella, FJ Cazorla 34th Euromicro Conference on Real-Time Systems (ECRTS 2022), 2022 | 6 | 2022 |
| NEUROPULS: NEUROmorphic energy-efficient secure accelerators based on Phase change materials aUgmented siLicon photonicS F Pavanello, C Marchand, I O'Connor, R Orobtchouk, F Mandorlo, ... arXiv preprint arXiv:2305.03139, 2023 | 3 | 2023 |
| Aleatoric Uncertainty Modelling in Regression Problems using Deep Learning A Brando Universitat de Barcelona, Available in axelbrando.github.io, 2022 | 3* | 2022 |
| On Neural Networks Redundancy and Diversity for Their Use in Safety-Critical Systems A Brando, I Serra, E Mezzetti, FJ Cazorla, J Perez, J Abella COMSI, 2023 | 2 | 2023 |
| Retrospective Uncertainties for Deep Models using Vine Copulas N Tagasovska, F Ozdemir, A Brando International Conference on Artificial Intelligence and Statistics, 2023 | 2 | 2023 |
| Standardizing the Probabilistic Sources of Uncertainty for the sake of Safety Deep Learning A Brando, I Serra, E Mezzetti, J Abella, FJ Cazorla AAAI's Workshop on AI Safety, 2023 | 2 | 2023 |
| Main sources of variability and non-determinism in AD software: taxonomy and prospects to handle them M Alcon, A Brando, E Mezzetti, J Abella, FJ Cazorla Real-Time Systems 59 (3), 438-478, 2023 | 1 | 2023 |
| SAFEXPLAIN: Safe and Explainable Critical Embedded Systems Based on AI J Abella Ferrer, J Perez, C Englund, B Zonooz, G Giordana, ... 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE …, 2023 | 1 | 2023 |
| Reclaiming uncertainties of deterministic deep models with Vine Copulas N Tagasovska, A Brando, F Ozdemir ICLR Workshop on Robust ML, 2021 | | 2021 |
| Detection of Balance Anomalies with Quantile Regression: the Power of Non-symmetry D Muelas, L Peinado, A Brando, JA Rodríguez-Serrano | | 2020 |
| Estudi de les xarxes neuronals convolucionals profundes mitjançant Caffe A Brando Guillaumes | | 2015 |
| Detecting Unusual Expense Categories for Financial Advice Apps A Brando, JA Rodríguez-Serrano, J Vitrià | | |
