Deep regression models typically learn in an end-to-end fashion without explicitly
emphasizing a regression-aware representation. Consequently, the learned representations …

Deep regression models typically learn in an end-to-end fashion and do not explicitly try to
learn a regression-aware representation. Their representations tend to be fragmented and …

Deep learning revolutionized data science, and recently its popularity has grown
exponentially, as did the amount of papers employing deep networks. Vision tasks, such as …

Contrastive learning methods can be applied to deep regression by enforcing label distance
relationships in feature space. However, these methods are limited to labeled data only …

In computer vision, it is often observed that formulating regression problems as a
classification task often yields better performance. We investigate this curious phenomenon …

It is common within the deep learning community to first pre-train a deep neural network from
a large-scale dataset and then fine-tune the pre-trained model to a specific downstream …

This work characterizes the effect of depth on the optimization landscape of linear
regression, showing that, despite their nonconvexity, deeper models have more desirable …

Representation learning that leverages large-scale labelled datasets, is central to recent
progress in machine learning. Access to task relevant labels at scale is often scarce or …

Despite being able to capture a range of features of the data, high accuracy models trained
with supervision tend to make similar predictions. This seemingly implies that high …

Many machine learning algorithms are trained and evaluated by splitting data from a single
source into training and test sets. While such focus on in-distribution learning scenarios has …