Negative sampling has swiftly risen to prominence as a focal point of research, with wide-
ranging applications spanning machine learning, computer vision, natural language …

Sentence representation learning is a crucial task in natural language processing, as the
quality of learned representations directly influences downstream tasks, such as sentence …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

This paper presents a whitening-based contrastive learning method for sentence
embedding learning (WhitenedCSE), which combines contrastive learning with a novel …

Contrastive sentence representation learning has made great progress thanks to a range of
text augmentation strategies and hard negative sampling techniques. However, most studies …

A core step of mining human mobility data is to learn accurate representations for user-
generated check-in sequences. The learned representations should be able to fully describe …

This paper presents miCSE, a mutual information-based contrastive learning framework that
significantly advances the state-of-the-art in few-shot sentence embedding. The proposed …

Current techniques for detecting AI-generated text are largely confined to manual feature
crafting and supervised binary classification paradigms. These methodologies typically lead …

Despite exciting progress in causal language models, the expressiveness of the
representations is largely limited due to poor discrimination ability. To remedy this issue, we …

Expressing universal semantics common to all languages is helpful to understand the
meanings of complex and culture-specific sentences. The research theme underlying this …