Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
Recent advances in Multi-modal Large Language Models (MLLMs) have shown significant progress in open-world Visual Question Answering (VQA). However, integrating visual information increases the number of processed tokens, leading to higher GPU memory usage and computational overhead. Images often contain more redundant information than text, and not all visual details are pertinent to specific q...
Knowledge Distillation (KD) compresses neural networks by learning a small network (student) via transferring knowledge from a pre-trained large network (teacher). Many endeavours have been devoted to the image domain, while few works focus on video analysis which desires training much larger model making it be hardly deployed in resource-limited devices. However, traditional methods neglect two...
The reproduction of state-of-the-art multimodal LLM pre-training faces barriers at every stage of the pipeline, including high-quality data filterin...
Change detection has essential significance for the region's development, in which pseudo-changes between bitemporal images induced by imaging envir...
This integrative literature review examines the evolving role of artificial intelligence (AI) and machine learning (ML) based clinical decision suppor...
Artificial intelligence (AI) is revolutionizing nursing by enhancing decision-making, patient monitoring, and efficiency. Machine learning, natural la...
BACKGROUND: Machine learning (ML) models can enhance patient-nurse assignments in healthcare organisations by learning from real data and identifying ...
Modern sensing and monitoring applications typically consist of sources transmitting updates of different sizes, ranging from a few bytes (position,...
Quantum generative models offer a promising new direction in machine learning by leveraging quantum circuits to enhance data generation capabilities...
Understanding the representation shift on Vision Language Models like CLIP under different augmentations provides valuable insights on Mechanistic I...
This paper presents a comparative analysis of distributed training strategies for large-scale neural networks, focusing on data parallelism, model p...
The widespread adoption of Large Language Models (LLMs) has enabled diverse applications with very different latency requirements. Existing LLM serv...
Recent advancements in text-to-image generative models, particularly latent diffusion models (LDMs), have demonstrated remarkable capabilities in sy...
As a fundamental task in computer vision, semantic segmentation is widely applied in fields such as autonomous driving, remote sensing image analysi...
Large-scale machine learning (ML) models are increasingly being used in critical domains like education, lending, recruitment, healthcare, criminal ...
Large language models accumulate vast knowledge during pre-training, yet the dynamics governing this acquisition remain poorly understood. This work...
The task of LiDAR-based 3D Open-Vocabulary Detection (3D OVD) requires the detector to learn to detect novel objects from point clouds without off-t...
Histopathology slide digitization introduces scanner-induced domain shift that can significantly impact computational pathology models based on deep...
As Large Language Models (LLMs) show their capabilities across various applications, training customized LLMs has become essential for modern enterp...
Existing unlearning algorithms in text-to-image generative models often fail to preserve the knowledge of semantically related concepts when removin...