Pre-trained language models can be fine-tuned to solve diverse NLP tasks, including in few-
shot settings. Thus fine-tuning allows the model to quickly pick up task-specific" skills," but …

The task of out-of-distribution (OOD) detection is crucial for deploying machine learning
models in real-world settings. In this paper, we observe that the singular value distributions …

The deployment constraints in practical applications necessitate the pruning of large-scale
deep learning models, ie, promoting their weight sparsity. As illustrated by the Lottery Ticket …

In response to recent data regulation requirements, machine unlearning (MU) has emerged
as a critical process to remove the influence of specific examples from a given model …

Although deep learning has made great progress in recent years, the exploding economic
and environmental costs of training neural networks are becoming unsustainable. To …

Despite their success, vision transformers still remain vulnerable to image corruptions, such
as noise or blur. Indeed, we find that the vulnerability mainly stems from the unstable self …

Despite their impressive performance on image classification tasks, deep networks have a
hard time generalizing to unforeseen corruptions of their data. To fix this vulnerability, prior …

Modern neural networks excel at image classification, yet they remain vulnerable to common
image corruptions such as blur, speckle noise or fog. Recent methods that focus on this …

We present a novel framework for generating adversarial benchmarks to evaluate the
robustness of image classification models. The RLAB framework allows users to customize …

State-of-the-art deep learning models have a parameter count that reaches into the billions.
Training, storing and transferring such models is energy and time consuming, thus costly. A …