Continual learning seeks to enable deep learners to train on a series of tasks of unknown
length without suffering from the catastrophic forgetting of previous tasks. One effective …

Recent studies have started to address the concern of detecting and rejecting the out-of-
distribution (OOD) samples as a major challenge in the safe deployment of deep learning …

Data heterogeneity presents significant challenges for federated learning (FL). Recently,
dataset distillation techniques have been introduced, and performed at the client level, to …

Finding a small set of representatives from an unlabeled dataset is a core problem in a
broad range of applications such as dataset summarization and information extraction …

Dataset distillation (DD) has emerged as a widely adopted technique for crafting a synthetic
dataset that captures the essential information of a training dataset, facilitating the training of …

We propose a novel technique for dataset summarization by selecting representatives from
a large, unsupervised dataset. The approach is based on the concept of self-rank, defined …

Training vision-based Autonomous driving models is a challenging problem with enormous
practical implications. One of the main challenges is the requirement of storage and …

The problem of simultaneous column and row subset selection is addressed in this paper.
The column space and row space of a matrix are spanned by its left and right singular …

We present a method to compute the derivative of a learning task with respect to a dataset. A
learning task is a function from a training set to the validation error, which can be …

The goal of simultaneous sparse representation is to capture as much information as
possible from a target matrix by a linear combination of several selected columns of another …