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, ... In the main task of tongue image ...

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 ...

Citation: Tack A, Shestakov A, Lüdke D and Zachow S (2021) A Multi-Task Deep Learning Method for Detection of Meniscal Tears in MRI Data from the Osteoarthritis Initiative Database. Front. Bioeng.

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 ...

In “Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding”, researchers Xiaodong Liu and Jianfeng Gao of Microsoft Research and Pengcheng He and ...

Chapter 9 is devoted to selected applications of deep learning to information retrieval including Web search. In Chapter 10, we cover selected applications of deep learning to image object recognition ...