A surprisingly sticky belief is that a machine learning model merely reflects existing
algorithmic bias in the dataset and does not itself contribute to harm. Why, despite clear …

Deep learning based models are relatively large, and it is hard to deploy such models on
resource-limited devices such as mobile phones and embedded devices. One possible …

Pruning-that is, setting a significant subset of the parameters of a neural network to zero-is
one of the most popular methods of model compression. Yet, several recent works have …

Model compression techniques are receiving increasing attention; however, the effect of
compression on model fairness is still under explored. This is the first paper to examine the …

Preserving long-tail, minority information during model compression has been linked to
algorithmic fairness considerations. However, this assumes that large models capture long …

Fully Homomorphic Encryption (FHE) is a cryptographic method that guarantees the privacy
and security of user data during computation. FHE algorithms can perform unlimited …

Network compression is now a mature sub-field of neural network research: over the last
decade, significant progress has been made towards reducing the size of models and …

Space weather phenomena like geomagnetic disturbances (GMDs) and geomagnetically
induced currents (GICs) pose significant risks to critical technological infrastructure. While …

In the rapidly evolving domain of Artificial Intelligence (AI), the complex interaction between
innovation and regulation has become an emerging focus of our society. Despite …

In the era of exceptionally data-hungry models, careful selection of the training data is
essential to mitigate the extensive costs of deep learning. Data pruning offers a solution by …