Atmospheric turbulence imposes a fundamental limitation across a broad range of applications, including optical imaging, remote sensing, and free-space optical ...
Predicting reaction outcomes across continuous solvent composition ranges remains a critical challenge in organic synthesis and process chemistry. Traditional m...
Understanding source code changes and their impact on other code entities is a crucial skill in software development. However, the analysis of code changes and ...
Recommender systems are enablers of personalized content delivery, and therefore revenue, for many large companies. In the last decade, deep learning recommende...
Have you ever wondered how a computer knows that a cat is more like a dog than a car? To a machine, words are just strings of characters or arbitrary ID numbers...
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Decentralized federated learning (DFL) enables collaborative model training across edge devices without centralized coordination, offering resilience against si...
Incorporating over-the-air computations (OAC) into the model training process of federated learning (FL) is an effective approach to alleviating the communicati...
Dynamic multimodal multiobjective optimization presents the dual challenge of simultaneously tracking multiple equivalent pareto optimal sets and maintaining po...
Catastrophic forgetting poses a fundamental challenge in continual learning, particularly when models are quantized for deployment efficiency. We systematically...
Differential privacy (DP) has emerged as the gold standard for protecting user data in recommender systems, but existing privacy-preserving mechanisms face a fu...
Artistic style transfer in generative models remains a significant challenge, as existing methods often introduce style only via model fine-tuning, additional a...
