Search Results (2)

Compressive Learning (CL) is an emerging paradigm that allows machine learning models to perform inference directly from compressed measurements, significantly reducing sensing and computational costs. While existing CL approaches have achieved competitive accuracy compared to traditional image-domain methods, they typically rely on reconstruction to address information loss and often neglect uncertainty arising from ambiguous or insufficient data. In this work, we propose MCS-TCL, a novel and trustworthy CL framework based on Multi-functional Compressive Sensing Sampling. Our approach unifies sampling, compression, and feature extraction into a single operation by leveraging the compatibility between compressive sensing and convolutional feature learning. This joint design enables efficient signal acquisition while preserving discriminative information, leading to feature representations that remain robust across varying sampling ratios. To enhance the model’s reliability, we incorporate evidential deep learning (EDL) during training. EDL estimates the distribution of evidence over output classes, enabling the model to quantify predictive uncertainty and assign higher confidence to well-supported predictions. Extensive experiments on image classification tasks show that MCS-TCL outperforms existing CL methods, achieving state-of-the-art accuracy at a low sampling rate of 6%. Additionally, our framework reduces model size by 85.76% while providing meaningful uncertainty estimates, demonstrating its effectiveness in resource-constrained learning scenarios. Full article

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease. Providing trustworthy AD progression predictions for at-risk individuals contributes to early identification of AD patients and holds significant value in discovering effective treatments and empowering the patient in taking proactive care. Recently, although numerous disease progression models based on machine learning have emerged, they often focus solely on enhancing predictive accuracy and ignore the measurement of result reliability. Consequently, this oversight adversely affects the recognition and acceptance of these models in clinical applications. To address these problems, we propose a multi-task evidential sequence learning model for the trustworthy prediction of disease progression. Specifically, we incorporate evidential deep learning into the multi-task learning framework based on recurrent neural networks. We simultaneously perform AD clinical diagnosis and cognitive score predictions while quantifying the uncertainty of each prediction without incurring additional computational costs by leveraging the Dirichlet and Normal-Inverse-Gamma distributions. Moreover, an adaptive weighting scheme is introduced to automatically balance between tasks for more effective training. Finally, experimental results on the TADPOLE dataset validate that our model not only has a comparable predictive performance to similar models but also offers reliable quantification of prediction uncertainties, providing a crucial supplementary factor for risk-sensitive AD progression prediction applications. Full article