Deep Regression

Deep regression uses deep learning models to predict continuous values, aiming to improve accuracy and efficiency compared to traditional regression methods. Current research focuses on enhancing model robustness and uncertainty quantification, employing architectures like convolutional neural networks, recurrent neural networks (LSTMs), and Gaussian processes, often combined with techniques like ensemble methods and post-hoc calibration. This field is significant for its applications across diverse domains, including medical imaging, agriculture, and materials science, enabling more accurate predictions and improved decision-making in these areas.