Deep learning proved its efficiency in many fields of computer science such as computer
vision, image classifications, object detection, image segmentation, and more. Deep …

Drug development is time‐consuming and expensive. Repurposing existing drugs for new
therapies is an attractive solution that accelerates drug development at reduced …

A membership inference attack allows an adversary to query a trained machine learning
model to predict whether or not a particular example was contained in the model's training …

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 …

Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

Generative AI technologies such as large language models show novel potential to enhance
educational research. For example, generative large language models were shown to be …

Learning and mining from imbalanced datasets gained increased interest in recent years.
One simple but efficient way to increase the performance of standard machine learning …

Abstract 3D shape is a crucial but heavily underutilized cue in today's computer vision
systems, mostly due to the lack of a good generic shape representation. With the recent …

In steerable filters, a filter of arbitrary orientation can be generated by a linear combination of
a set of “basis filters.” Steerable properties dominate the design of the traditional filters, eg …

The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all
observations, model parameters, and model structure using probability theory. Probabilistic …