Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
With the rapid growth of Internet services, recommendation systems play a central role in delivering personalized content. Faced with massive user requests and complex model architectures, the key challenge for real-time recommendation systems is how to reduce inference latency and increase system throughput without sacrificing recommendation quality. This paper addresses the high computational ...
Accurate automatic screening of minors in unconstrained images demands models that are robust to distribution shift and resilient to the children under-representation in publicly available data. To overcome these issues, we propose a multi-task architecture with dedicated under/over-age discrimination tasks based on a frozen FaRL vision-language backbone joined with a compact two-layer MLP that ...
Diffusion distillation is a widely used technique to reduce the sampling cost of diffusion models, yet it often requires extensive training, and the...
Diffusion models (DMs) have achieved significant progress in text-to-image generation. However, the inevitable inclusion of sensitive information du...
Safe deployment of machine learning (ML) models in safety-critical domains such as medical imaging requires detecting inputs with characteristics no...
Autonomous agents powered by multimodal large language models have been developed to facilitate task execution on mobile devices. However, prior wor...
Traditional algorithms to optimize artificial neural networks when confronted with a supervised learning task are usually exploitation-type relaxati...
A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-e...
In many settings in science and industry, such as drug discovery and clinical trials, a central challenge is designing experiments under time and bu...
Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled powerful autonomous agents capable of complex reasoni...
While diffusion models have demonstrated remarkable generative capabilities, existing style transfer techniques often struggle to maintain identity ...
Signal restoration and inverse problems are key elements in most real-world data science applications. In the past decades, with the emergence of ma...
The development of modern Artificial Intelligence (AI) models, particularly diffusion-based models employed in computer vision and image generation ...
Among the genetic algorithms generally used for optimization problems in the recent decades, quantum-inspired variants are known for fast and high-f...
Recent work has shown improved lesion detectability and flexibility to reconstruction hyperparameters (e.g. scanner geometry or dose level) when PET...
Lightweight containers provide an efficient approach for deploying computation-intensive applications in network edge. The layered storage structure...
Universal Domain Adaptation (UniDA) focuses on transferring source domain knowledge to the target domain under both domain shift and unknown categor...
Personalized federated learning (PFL) offers a solution to balancing personalization and generalization by conducting federated learning (FL) to gui...
H nuclear magnetic resonance (NMR) spectroscopy plays an important role in the pharmaceutical industry, but for complex substances, spectral analysis ...
Large foundation models trained on extensive datasets demonstrate strong zero-shot capabilities in various domains. To replicate their success when ...