@inproceedings{dd267fc942ea43c088ae2e230ecc7299,
title = "Deep robust regression",
abstract = "In this paper, we consider the use of deep neural networks in the context of robust regression. We address the standard linear model with observations that are corrupted by outliers. We build upon Huber's robust regression and the classical least trimmed squares estimator, and propose a deep neural network that generalizes both and provides high accuracy with low computational complexity. The network is trained for arbitrary linear models using a single training phase. Numerical experiments with synthetic data demonstrate that the network can handle on a large range of Signal-to-Noise Ratio (SNR) and is robust to different types of outliers.",
keywords = "Deep Learning, Neural Networks, Robust Regression",
author = "Tzvi Diskin and Gordana Draskovic and Frederic Pascal and Ami Wiesel",
note = "Funding Information: This research was partly supported by ISF grant 1339/15. Publisher Copyright: {\textcopyright} 2017 IEEE.; 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 ; Conference date: 10-12-2017 Through 13-12-2017",
year = "2018",
month = mar,
day = "9",
doi = "10.1109/CAMSAP.2017.8313200",
language = "American English",
series = "2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--5",
booktitle = "2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017",
address = "United States",
}