| Reconsidering Linear Transmit Signal Processing in 1-Bit Quantized Multi-User MISO Systems O De Candido, H Jedda, A Mezghani, AL Swindlehurst, JA Nossek IEEE Transactions on Wireless Communications 18 (1), 254-267, 2019 | 18 | 2019 |
| Interpretable feature generation using deep neural networks and its application to lane change detection O Gallitz, O De Candido, M Botsch, W Utschick 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 3405-3411, 2019 | 15 | 2019 |
| Towards Feature Validation in Time to Lane Change Classification using Deep Neural Networks O De Candido, M Koller, O Gallitz, R Melz, M Botsch, W Utschick 2020 IEEE 23rd International Conference on Intelligent Transportation …, 2020 | 10 | 2020 |
| SIMO/MISO MSE-duality for multi-user FBMC with highly frequency selective channels O De Candido, LG Baltar, A Mezghani, JA Nossek WSA 2015; 19th International ITG Workshop on Smart Antennas, 1-7, 2015 | 9 | 2015 |
| Parameter sharing reinforcement learning for modeling multi-agent driving behavior in roundabout scenarios F Konstantinidis, M Sackmann, O De Candido, U Hofmann, J Thielecke, ... 2021 IEEE International Intelligent Transportation Systems Conference (ITSC …, 2021 | 8 | 2021 |
| An interpretable lane change detector algorithm based on deep autoencoder anomaly detection O De Candido, M Binder, W Utschick 2021 IEEE Intelligent Vehicles Symposium (IV), 516-523, 2021 | 8 | 2021 |
| Downlink precoder and equalizer designs for multi-user MIMO FBMC/OQAM O De Candido, SA Cheema, LG Baltar, M Haardt, JA Nossek WSA 2016; 20th International ITG Workshop on Smart Antennas, 1-8, 2016 | 7 | 2016 |
| Interpretable machine learning structure for an early prediction of lane changes O Gallitz, O De Candido, M Botsch, R Melz, W Utschick Artificial Neural Networks and Machine Learning–ICANN 2020: 29th …, 2020 | 6 | 2020 |
| Interpretable classifiers based on time-series motifs for lane change prediction K Klein, O De Candido, W Utschick IEEE Transactions on Intelligent Vehicles, 2023 | 2 | 2023 |
| An analysis of distributional shifts in automated driving functions in highway scenarios O De Candido, X Li, W Utschick 2022 IEEE 95th Vehicular Technology Conference:(VTC2022-Spring), 1-7, 2022 | 2 | 2022 |
| Encouraging Validatable Features in Machine Learning-Based Highly Automated Driving Functions O De Candido, M Koller, W Utschick IEEE Transactions on Intelligent Vehicles 8 (2), 1837-1851, 2022 | 2 | 2022 |
| Are traditional signal processing techniques rate maximizing in quantized SU-MISO systems? O De Candido, H Jedda, A Mezghani, AL Swindlehurst, JA Nossek GLOBECOM 2017-2017 IEEE Global Communications Conference, 1-6, 2017 | 2 | 2017 |
| DFE/THP duality for FBMC with highly frequency selective channels H Jedda, LG Baltar, O De Candido, A Mezghani, JA Nossek 2015 23rd European Signal Processing Conference (EUSIPCO), 2127-2131, 2015 | 2 | 2015 |
| Detecting an Offset-Adjusted Similarity Score based on Duchenne Smiles M Henneberg, C Eghtebas, O De Candido, K Kunze, JA Ward Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing …, 2023 | 1 | 2023 |
| Classification and uncertainty quantification of corrupted data using supervised autoencoders P Joppich, S Dorn, O De Candido, J Knollmüller, W Utschick Physical Sciences Forum 5 (1), 12, 2022 | 1 | 2022 |
| Interpretable early prediction of lane changes using a constrained neural network architecture O Gallitz, O De Candido, M Botsch, W Utschick 2021 IEEE International Intelligent Transportation Systems Conference (ITSC …, 2021 | 1 | 2021 |
| Learning to Detect Adversarial Examples Based on Class Scores T Uelwer, F Michels, O De Candido KI 2021: Advances in Artificial Intelligence: 44th German Conference on AI …, 2021 | 1 | 2021 |
| On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows JY Tee, O De Candido, W Utschick, P Geiger 2023 IEEE 26th International Conference on Intelligent Transportation …, 2023 | | 2023 |
| Validating Machine Learning-based Highly Automated Driving Functions by Diversity OT De Candido Technische Universität München, 2023 | | 2023 |
| Evaluating Robust Perceptual Losses for Image Reconstruction T Uelwer, F Michels, O De Candido I Can't Believe It's Not Better Workshop: Understanding Deep Learning …, 2022 | | 2022 |
