Nuclear-derived morphological features and biomarkers provide relevant insights regarding
the tumour microenvironment, while also allowing diagnosis and prognosis in specific …

We study the problem of (learning) algorithm comparison, where the goal is to find
differences between models trained with two different learning algorithms. We begin by …

Due to the limited availability of data, existing few-shot learning methods trained from
scratch fail to achieve satisfactory performance. In contrast, large-scale pre-trained models …

Model inversion, which aims to reconstruct the original training data from pre-trained
discriminative models, is especially useful when the original training data is unavailable due …

Transformers achieve state-of-the-art accuracy and robustness across many tasks, but an
understanding of the inductive biases that they have and how those biases are different from …

Models trained on data composed of different groups or domains can suffer from severe
performance degradation under distribution shifts. While recent methods have largely …

The robustness of machine learning algorithms to distributions shift is primarily discussed in
the context of supervised learning (SL). As such, there is a lack of insight on the robustness …

Abstract Vision Transformer (ViT) has recently gained significant attention in solving
computer vision (CV) problems due to its capability of extracting informative features and …

Domain generalization (DG) intends to train a model on multiple source domains to ensure
that it can generalize well to an arbitrary unseen target domain. The acquisition of domain …

Few-shot image classifiers are designed to recognize and classify new data with minimal
supervision and limited data but often show reliance on spurious correlations between …