Zobrazeno 1 - 10
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pro vyhledávání: '"CLIFTON, DAVID A."'
Online continual learning (OCL) seeks to learn new tasks from data streams that appear only once, while retaining knowledge of previously learned tasks. Most existing methods rely on replay, focusing on enhancing memory retention through regularizati
Externí odkaz:
http://arxiv.org/abs/2412.18177
Stress is a pervasive global health issue that can lead to severe mental health problems. Early detection offers timely intervention and prevention of stress-related disorders. The current early detection models perform "black box" inference sufferin
Externí odkaz:
http://arxiv.org/abs/2412.14009
Shared training approaches, such as multi-task learning (MTL) and gradient-based meta-learning, are widely used in various machine learning applications, but they often suffer from negative transfer, leading to performance degradation in specific tas
Externí odkaz:
http://arxiv.org/abs/2412.04413
Autor:
Saha, Pramit, Wagner, Felix, Mishra, Divyanshu, Peng, Can, Thakur, Anshul, Clifton, David, Kamnitsas, Konstantinos, Noble, J. Alison
Effective training of large Vision-Language Models (VLMs) on resource-constrained client devices in Federated Learning (FL) requires the usage of parameter-efficient fine-tuning (PEFT) strategies. To this end, we demonstrate the impact of two factors
Externí odkaz:
http://arxiv.org/abs/2411.11912
Large language models (LLMs) are emerging as promising tools for mental health care, offering scalable support through their ability to generate human-like responses. However, the effectiveness of these models in clinical settings remains unclear. Th
Externí odkaz:
http://arxiv.org/abs/2408.11288
Autor:
Anibal, James, Gunkel, Jasmine, Awan, Shaheen, Huth, Hannah, Nguyen, Hang, Le, Tram, Bélisle-Pipon, Jean-Christophe, Boyer, Micah, Hazen, Lindsey, Consortium, Bridge2AI Voice, Bensoussan, Yael, Clifton, David, Wood, Bradford
Artificial intelligence (AI) methods have been proposed for the prediction of social behaviors which could be reasonably understood from patient-reported information. This raises novel ethical concerns about respect, privacy, and control over patient
Externí odkaz:
http://arxiv.org/abs/2408.07896
Utilizing large pre-trained models for specific tasks has yielded impressive results. However, fully fine-tuning these increasingly large models is becoming prohibitively resource-intensive. This has led to a focus on more parameter-efficient transfe
Externí odkaz:
http://arxiv.org/abs/2408.02421
Autor:
Gowda, Shreyank N, Clifton, David A.
The Segment Anything Model (SAM) has achieved remarkable successes in the realm of natural image segmentation, but its deployment in the medical imaging sphere has encountered challenges. Specifically, the model struggles with medical images that fea
Externí odkaz:
http://arxiv.org/abs/2408.00181
Autor:
Gowda, Shreyank N, Clifton, David A.
Contemporary medical contrastive learning faces challenges from inconsistent semantics and sample pair morphology, leading to dispersed and converging semantic shifts. The variability in text reports, due to multiple authors, complicates semantic con
Externí odkaz:
http://arxiv.org/abs/2407.16264
Autor:
Wentzel, Andrew, Attia, Serageldin, Zhang, Xinhua, Canahuate, Guadalupe, Fuller, Clifton David, Marai, G. Elisabeta
Digital twin models are of high interest to Head and Neck Cancer (HNC) oncologists, who have to navigate a series of complex treatment decisions that weigh the efficacy of tumor control against toxicity and mortality risks. Evaluating individual risk
Externí odkaz:
http://arxiv.org/abs/2407.13107