Large language models (LLMs) have garnered unprecedented advancements across
diverse fields, ranging from natural language processing to computer vision and beyond …

This study investigates the integration of Retrieval Augmented Generation (RAG) into the
Mistral 8x7B Large Language Model (LLM), which already uses Mixture of Experts (MoE), to …

In this paper, we present DiT-MoE, a sparse version of the diffusion Transformer, that is
scalable and competitive with dense networks while exhibiting highly optimized inference …

Rapid urbanization has significantly escalated traffic congestion, underscoring the need for
advanced congestion prediction services to bolster intelligent transportation systems. As one …

Mixture-of-Experts (MoE) has gained increasing popularity as a promising framework for
scaling up large language models (LLMs). However, the reliability assessment of MoE lags …

Brain lesion segmentation plays an essential role in neurological research and diagnosis.
As brain lesions can be caused by various pathological alterations, different types of brain …

By increasing model parameters but activating them sparsely when performing a task, the
use of Mixture-of-Experts (MoE) architecture significantly improves the performance of Large …

Although large language models (LLMs) have achieved significant success, their
vulnerability to adversarial perturbations, including recent jailbreak attacks, has raised …

Adversarial attacks have been proven to be potential threats to Deep Neural Networks
(DNNs), and many methods are proposed to defend against adversarial attacks. However …

Recent advancements in diffusion models have made generative image editing more
accessible, enabling creative edits but raising ethical concerns, particularly regarding …