Transformer models have emerged as the leading approach for achieving
st...
Due to the recent success of diffusion models, text-to-image generation ...
The high computational and memory requirements of generative large langu...
With the increasing data volume, there is a trend of using large-scale
p...
Recent years have witnessed the unprecedented achievements of large-scal...
Transformer models have achieved state-of-the-art performance on various...
Graph neural networks (GNNs) are a type of deep learning models that lea...
Contrastive Language-Image Pre-training (CLIP) has been shown to learn v...
Large-scale deep learning models contribute to significant performance
i...
Vertical federated learning (VFL) is an emerging paradigm that allows
di...
As giant dense models advance quality but require large-scale expensive ...
Mixture-of-experts (MoE) is becoming popular due to its success in impro...
Embedding models have been an effective learning paradigm for
high-dimen...
Recently, zero-shot and few-shot learning via Contrastive Vision-Languag...