IEEE

Toward Model-Independent Separative Training for Deep Hyperspectral Anomaly Detection With Mask Guidance

Abstract: Hyperspectral anomaly detection (HAD) aims to recognize a minority of anomalies that are spectrally different from their surrounding background without prior knowledge. Deep neural networks ...
Microsoft

Scalable and Efficient MoE Training for Multitask Multilingual Models

The Mixture of Experts (MoE) models are an emerging class of sparsely activated deep learning models that have sublinear compute costs with respect to their parameters. In contrast with dense models, ...
IEEE

Development of Field-Deployable Detection Models for Asbestos Host Rocks Using Close-Range Hyperspectral Imaging and Diverse Lithological Training Data

Abstract: This study presents a field-applicable detection models for host rocks of naturally occurring asbestos (NOA) through hyperspectral imaging and machine learning. We developed detection models ...