Self-supervised learning (SSL) is a powerful technique for learning from unlabeled data. By
learning to remain invariant to applied data augmentations, methods such as SimCLR and …

This work leverages neural transformers to generate hierarchies in an existing knowledge
graph. For small (< 10,000 node) domain-specific KGs, we find that a combination of few …

In the face of rapidly accumulating genomic data, our ability to accurately predict key mature
RNA properties that underlie transcript function and regulation remains limited. Pre-trained …

In-context learning (ICL) capabilities are foundational to the success of large language
models (LLMs). Recently, context compression has attracted growing interest since it can …

Supervised machine learning and deep learning require a large amount of labeled data,
which data scientists obtain in a manual, and time-consuming annotation process. To …

Abstract Analysis of histopathological images allows doctors to diagnose diseases like
cancer, which is the cause of nearly one in six deaths worldwide. Classification of such …

In traffic safety analysis, previous research has often focused on tabular data or textual crash
narratives in isolation, neglecting the potential benefits of a hybrid multimodal approach …

Современные алгоритмы глубокого обучения позволяют достичь высоких результатов
предсказания в различных задачах классификации изображений. Как правило, для …

Supervised machine learning and deep learning require a large amount of labeled data,
which data scientists obtain in a manual, and time-consuming annotation process. To …

Recently, it has been stipulated that training larger and larger models, using ever increasing
datasets is not sustainable in a long-run. Hence, the idea of Frugal Artificial Intelligence has …