Deep learning has evolved as a strong and efficient framework that can be applied to a
broad spectrum of complex learning problems which were difficult to solve using the …

The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

While machine learning (ML) models are being increasingly trusted to make decisions in
different and varying areas, the safety of systems using such models has become an …

Deep learning has emerged as a strong and efficient framework that can be applied to a
broad spectrum of complex learning problems which were difficult to solve using the …

Deep neural networks and machine-learning algorithms are pervasively used in several
applications, ranging from computer vision to computer security. In most of these …

Multimodal contrastive learning methods like CLIP train on noisy and uncurated training
datasets. This is cheaper than labeling datasets manually, and even improves out-of …

In recent years, artificial intelligence technologies have been widely used in computer
vision, natural language processing, automatic driving, and other fields. However, artificial …

Hyperparameters are critical in machine learning, as different hyperparameters often result
in models with significantly different performance. Hyperparameters may be deemed …

Machine learning (ML) models may be deemed confidential due to their sensitive training
data, commercial value, or use in security applications. Increasingly often, confidential ML …

Many machine learning models are vulnerable to adversarial examples: inputs that are
specially crafted to cause a machine learning model to produce an incorrect output …