Oliver De Candido

Orcid: 0000-0002-9523-7777

According to our database1, Oliver De Candido authored at least 18 papers between 2015 and 2023.

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

Timeline

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Links

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Bibliography

2023
Interpretable Classifiers Based on Time-Series Motifs for Lane Change Prediction.
IEEE Trans. Intell. Veh., July, 2023

Encouraging Validatable Features in Machine Learning-Based Highly Automated Driving Functions.
IEEE Trans. Intell. Veh., February, 2023

On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows.
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023

Detecting an Offset-Adjusted Similarity Score based on Duchenne Smiles.
Proceedings of the Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

2022
An Analysis of Distributional Shifts in Automated Driving Functions in Highway Scenarios.
Proceedings of the 95th IEEE Vehicular Technology Conference, 2022

2021
Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders.
CoRR, 2021

Learning to Detect Adversarial Examples Based on Class Scores.
Proceedings of the KI 2021: Advances in Artificial Intelligence - 44th German Conference on AI, Virtual Event, September 27, 2021

An Interpretable Lane Change Detector Algorithm based on Deep Autoencoder Anomaly Detection.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2021

Parameter Sharing Reinforcement Learning for Modeling Multi-Agent Driving Behavior in Roundabout Scenarios.
Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference, 2021

Interpretable Early Prediction of Lane Changes Using a Constrained Neural Network Architecture.
Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference, 2021

2020
Towards Feature Validation in Time to Lane Change Classification using Deep Neural Networks.
Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems, 2020

Interpretable Machine Learning Structure for an Early Prediction of Lane Changes.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2020, 2020

2019
Reconsidering Linear Transmit Signal Processing in 1-Bit Quantized Multi-User MISO Systems.
IEEE Trans. Wirel. Commun., 2019

Interpretable Feature Generation using Deep Neural Networks and its Application to Lane Change Detection.
Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, 2019

2017
Are Traditional Signal Processing Techniques Rate Maximizing in Quantized SU-MISO Systems?
Proceedings of the 2017 IEEE Global Communications Conference, 2017

2016
Downlink Precoder and Equalizer Designs for Multi-User MIMO FBMC/OQAM.
Proceedings of the WSA 2016, 2016

2015
SIMO/MISO MSE-Duality for Multi-User FBMC with Highly Frequency Selective Channels.
Proceedings of the WSA 2015, 2015

DFE/THP duality for FBMC with highly frequency selective channels.
Proceedings of the 23rd European Signal Processing Conference, 2015


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