In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …

Word representation has always been an important research area in the history of natural
language processing (NLP). Understanding such complex text data is imperative, given that …

Recent self-supervised methods for image representation learning are based on maximizing
the agreement between embedding vectors from different views of the same image. A trivial …

Unsupervised image representations have significantly reduced the gap with supervised
pretraining, notably with the recent achievements of contrastive learning methods. These …

This paper presents Prototypical Contrastive Learning (PCL), an unsupervised
representation learning method that addresses the fundamental limitations of instance-wise …

With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but,
typically, only a small fraction of spectra can be matched. Previous in silico methods search …

Automatic differentiation (autodiff) has revolutionized machine learning. Itallows to express
complex computations by composing elementary ones in creativeways and removes the …

The de-facto approach to many vision tasks is to start from pretrained visual representations,
typically learned via supervised training on ImageNet. Recent methods have explored …

Perceptual constancy requires the brain to maintain a stable representation of sensory input.
In the olfactory system, activity in primary olfactory cortex (piriform cortex) is thought to …