Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …

Since deep neural networks were developed, they have made huge contributions to
everyday lives. Machine learning provides more rational advice than humans are capable of …

Abstract Pretraining Vision Transformers (ViTs) has achieved great success in visual
recognition. A following scenario is to adapt a ViT to various image and video recognition …

Modeling flexible macromolecules is one of the foremost challenges in single-particle
cryogenic-electron microscopy (cryo-EM), with the potential to illuminate fundamental …

Large pretrained language models have shown surprising in-context learning (ICL) ability.
With a few demonstration input-label pairs, they can predict the label for an unseen input …

The recently proposed FixMatch achieved state-of-the-art results on most semi-supervised
learning (SSL) benchmarks. However, like other modern SSL algorithms, FixMatch uses a …

The influential Residual Networks designed by He et al. remain the gold-standard
architecture in numerous scientific publications. They typically serve as the default …

Learning with few labeled data has been a longstanding problem in the computer vision and
machine learning research community. In this paper, we introduced a new semi-supervised …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

In the last decade, convolutional neural networks (ConvNets) have been a major focus of
research in medical image analysis. However, the performances of ConvNets may be limited …