The internal structure and operation mechanism of large-scale language models are
analyzed theoretically, especially how Transformer and its derivative architectures can …

The research explores the utilization of a deep learning model employing an attention
mechanism in medical text mining. It targets the challenge of analyzing unstructured text …

This research delves deeply into Meta Reinforcement Learning (Meta RL) through a
exploration focusing on defining generalization limits and ensuring convergence. By …

This paper introduces an innovative multi-modal fusion deep learning approach to
overcome the drawbacks of traditional single-modal recognition techniques. These …

In this research, we introduce an innovative method for synthesizing medical images using
generative adversarial networks (GANs). Our proposed GANs method demonstrates the …

Pulmonary nodules serve as critical indicators for early lung cancer diagnosis, making their
detection and classification essential. The prevalent use of transfer learning in recognition …

Abstract Evaluation of generative foundation models (GenFMs) for text-to-visual tasks has
been enhanced by automatic alignment metrics such as CLIPScore complementing human …

While text-to-video (T2V) generative models produce exceptionally realistic videos they lack
a comprehensive evaluation across the temporal dimension with a limited focus on basic …

Aiming at the limitations of traditional medical decision system in processing large-scale
heterogeneous medical data and realizing highly personalized recommendation, this paper …

This paper focuses on the application and optimization of LSTM model in financial risk
prediction. The study starts with an overview of the architecture and algorithm foundation of …