Axel Brando

Orcid: 0000-0001-8103-391X

According to our database1, Axel Brando authored at least 14 papers between 2018 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2023
Main sources of variability and non-determinism in AD software: taxonomy and prospects to handle them.
Real Time Syst., September, 2023

On Neural Networks Redundancy and Diversity for Their Use in Safety-Critical Systems.
Computer, May, 2023

NEUROPULS: NEUROmorphic energy-efficient secure accelerators based on Phase change materials aUgmented siLicon photonicS.
CoRR, 2023



Retrospective Uncertainties for Deep Models using Vine Copulas.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Standardizing the Probabilistic Sources of Uncertainty for the sake of Safety Deep Learning.
Proceedings of the Workshop on Artificial Intelligence Safety 2023 (SafeAI 2023) co-located with the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023), 2023

2022
Aleatoric Uncertainty Modelling in Regression Problems using Deep Learning
PhD thesis, 2022

Using Quantile Regression in Neural Networks for Contention Prediction in Multicore Processors.
Proceedings of the 34th Euromicro Conference on Real-Time Systems, 2022

Deep Non-crossing Quantiles through the Partial Derivative.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2020
Building Uncertainty Models on Top of Black-Box Predictive APIs.
IEEE Access, 2020

2019
Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Uncertainty Estimation for Black-Box Classification Models: A Use Case for Sentiment Analysis.
Proceedings of the Pattern Recognition and Image Analysis - 9th Iberian Conference, 2019

2018
Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018


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