Deep neural networks (DNNs) are machine learning algorithms that have revolutionized
computer vision due to their remarkable successes in tasks like object classification and …

Abstract Machine learning models have been found to learn shortcuts---unintended decision
rules that are unable to generalize---undermining models' reliability. Previous works address …

Pre-trained language models (PLMs) are known to improve the generalization performance
of natural language understanding models by leveraging large amounts of data during the …

Synthetic image datasets offer unmatched advantages for designing and evaluating deep
neural networks: they make it possible to (i) render as many data samples as needed,(ii) …

Self-supervised learning (SSL) is a machine learning approach where the data itself
provides supervision, eliminating the need for external labels. The model is forced to learn …

Deep learning is closing the gap with human vision on several object recognition
benchmarks. Here we investigate this gap in the context of challenging images where …

Supervised models based on Transformers have been shown to achieve impressive
performances in many natural language processing tasks. However, besides requiring a …

Geometric deep learning (GDL) has gained significant attention in various scientific fields,
chiefly for its proficiency in modeling data with intricate geometric structures. Yet, very few …

Symmetries (transformations by group actions) are present in many datasets, and leveraging
them holds significant promise for improving predictions in machine learning. In this work …

Deep learning is closing the gap with humans on several object recognition benchmarks.
Here we investigate this gap in the context of challenging images where objects are seen …