Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …

The question answering system is frequently applied in the area of natural language
processing (NLP) because of the wide variety of applications. It consists of answering …

Abstract Contrastive Language-Image Pre-training (CLIP) stands as one of the most effective
and scalable methods for training transferable vision models using paired image and text …

Adapter-based tuning has recently arisen as an alternative to fine-tuning. It works by adding
light-weight adapter modules to a pretrained language model (PrLM) and only updating the …

Inductive transfer learning has greatly impacted computer vision, but existing approaches in
NLP still require task-specific modifications and training from scratch. We propose Universal …

While most previous work has focused on different pretraining objectives and architectures
for transfer learning, we ask how to best adapt the pretrained model to a given target task …

Many modern NLP systems rely on word embeddings, previously trained in an unsupervised
manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of …

This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a
dataset designed for use in the development and evaluation of machine learning models for …

Inferring the structural properties of a protein from its amino acid sequence is a challenging
yet important problem in biology. Structures are not known for the vast majority of protein …

Deep pretrained language models have achieved great success in the way of pretraining
first and then fine-tuning. But such a sequential transfer learning paradigm often confronts …