Few-shot classification is a challenging problem due to the uncertainty caused by using few
labelled samples. In the past few years, methods have been proposed to solve few-shot …

In many real-life problems, it is difficult to acquire or label large amounts of data, resulting in
so-called few-shot learning problems. However, few-shot classification is a challenging …

Artificial Intelligence (AI) is increasingly sha** the field of dermatology, particularly in the
detection and management of skin cancers, including melanoma, basal cell carcinoma …

In the field of machine learning, deep neural networks have become the
inescapablereference for a very large number of problems. These systems are made of an …

Learning from a limited number of samples is challenging since the learned model can
easily become overfitted based on the biased distribution formed by only a few training …

A first step is taken towards understanding often observed non-robustness phenomena of
deep neural net (DNN) classifiers. This is done from the perspective of Boolean functions by …

In solving learning problems with limited training data, few-shot learning is proposed to
remember some common knowledge by leveraging a large number of similar few-shot tasks …

Interestingly, when I began my PhD back in 2008, neural networks were almost unanimously
considered obsolete. Their inaptitude to solve real world problems, added to the …