Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

Unsupervised person re-identification (re-ID) aims at learning discriminative representations
for person retrieval from unlabeled data. Recent techniques accomplish this task by using …

Pretrained language models (PLMs) have demonstrated remarkable performance in various
natural language processing tasks: Unidirectional PLMs (eg, GPT) are well known for their …

Recently, over-parameterized deep networks, with increasingly more network parameters
than training samples, have dominated the performances of modern machine learning …

The similarity among samples and the discrepancy among clusters are two crucial aspects
of image clustering. However, current deep clustering methods suffer from inaccurate …

Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a
lightweight student model, has been investigated to design efficient neural architectures …

Self-supervised contrastive representation learning has proved incredibly successful in the
vision and natural language domains, enabling state-of-the-art performance with orders of …

Abstract The Randomized Response (RR) algorithm is a classical technique to improve
robustness in survey aggregation, and has been widely adopted in applications with …

Neural named entity recognition (NER) models may easily encounter the over-confidence
issue, which degrades the performance and calibration. Inspired by label smoothing and …