This paper studies task-oriented, otherwise known as goal-oriented, communications, in a setting where a trans-mitter communicates with multiple receivers, each with its own task to complete on a ...

We then designed a multi-task deep learning network to classify and evaluate the quality of tongue images by adding tongue segmentation as an auxiliary task, as the two tasks are related and can ...

Framing the problem of meniscal tear detection in this multi-task learning setting – simultaneously solving meniscal tear detection and meniscal bounding box regression – allows our model to ...

The event-related potential encoder network (ERPENet) is a multi-task autoencoder-based model, that can be applied to any ERP-related tasks. model.py -- contains all model builders in Keras. train.py ...

Watch us compress multiple ensembled models into a single Multi-Task Deep Neural Network via knowledge distillation for learning robust and universal text representations across multiple natural ...

Many previous studies have proposed multi-task learning methods to jointly tackle tumor segmentation and classification by sharing the features extracted by the encoder. Unfortunately, this often ...

New multi-task deep learning framework integrates large-scale single-cell proteomics and transcriptomics data by Zhang Nannan, Chinese Academy of Sciences Integration of COVID-19 cell atlas.