Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

Despite the efficiency and scalability of machine learning systems, recent studies have
demonstrated that many classification methods, especially Deep Neural Networks (DNNs) …

Transfer learning is a widely-used paradigm in deep learning, where models pre-trained on
standard datasets can be efficiently adapted to downstream tasks. Typically, better pre …

Classically, data interpolation with a parametrized model class is possible as long as the
number of parameters is larger than the number of equations to be satisfied. A puzzling …

Despite the great empirical success of deep reinforcement learning, its theoretical
foundation is less well understood. In this work, we make the first attempt to theoretically …

The goal of data attribution is to trace model predictions back to training data. Despite a long
line of work towards this goal, existing approaches to data attribution tend to force users to …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

It is commonly believed that networks cannot be both accurate and robust, that gaining
robustness means losing accuracy. It is also generally believed that, unless making …

Classically, data interpolation with a parametrized model class is possible as long as the
number of parameters is larger than the number of equations to be satisfied. A puzzling …

Adversarial training is one of the main defenses against adversarial attacks. In this paper,
we provide the first rigorous study on diagnosing elements of adversarial training, which …