Contrastive Learning has recently received interest due to its success in self-supervised
representation learning in the computer vision domain. However, the origins of Contrastive …

In the last decade, deep supervised learning has had tremendous success. However, its
flaws, such as its dependency on manual and costly annotations on large datasets and …

Text embeddings are useful features in many applications such as semantic search and
computing text similarity. Previous work typically trains models customized for different use …

Pre-trained language models have proven their unique powers in capturing implicit
language features. However, most pre-training approaches focus on the word-level training …

Large language models trained on massive code corpora can generalize to new tasks
without the need for task-specific fine-tuning. In few-shot learning, these models take as …

A transcompiler, also known as source-to-source translator, is a system that converts source
code from a high-level programming language (such as C++ or Python) to another …

Automated code generation can be a powerful technique for software development,
significantly reducing developers' efforts and time required to create new code by generating …

Machine learning and its promising branch deep learning have shown success in a wide
range of application domains. Recently, much effort has been expended on applying deep …

Code completion, which aims to predict the following code token (s) according to the code
context, can improve the productivity of software development. Recent work has proved that …

Code representation learning, which aims to encode the semantics of source code into
distributed vectors, plays an important role in recent deep-learning-based models for code …