This research presents a novel deep multi-task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and t
2.1. Energy multi-task learning Modern smart buildings are equipped with multiple types of sensors, such as heating, cooling, electricity load, gas load etc. There are massive amounts of multi-variable data generated from these sensors, and multi-task prediction models are developed to address hi...
Multi-task learning (MTL)14is a learning paradigm that aims to improve generalization by using the domain information contained in the training signals of related tasks as an inductive bias. In practice, this is done by training a shared model for all tasks. In deep MTL, the shared model co...
We made this decision as integral regression is extremely fast at inference time and requires no additional loss term or costly optimization of an additional output target, thereby speeding up training and decreasing instability inherent in multi-task learning. Closed-loop control Our closed-loop ...
With the introduction of an efficient feature extraction module, a hybrid CNN-Transformer classifier, and a multi-task loss function, the proposed multi-task deep learning network significantly enhances the performance achieved in both GTV segmentation and EGFR genotyping tasks. Thus, the model can se...
Task1: Translate paragraph to Chinese with good format Translated Paragraph 9: 系统实现 应用推荐流程的实现包括三个阶段:数据生成、模型训练和模型服务,如图3所示。 Summary: 系统实现概述 应用推荐流程的实现分为三个主要阶段。 三个阶段的详细描述 这三个阶段分别是:数据生成、模型训练和模型服务。 图2 图3...
computation offloading; deep learning; CNN; edge computing; face recognition; parallelism; pipelining; multithreading; multitask learning; SIMD and MIMD architecture; VGG-Face; YOLOv81. Introduction Face detection and analysis techniques are being targeted as a biometric verification tool to address ...
Deep Learning with Theano - Part 1: Logistic RegressionOver the last ten years the subject of deep learning has been one of the most discussed fields in machine learning and artificial intelligence. It has produced state-of-the-art results in areas as diverse as computer vision, image ...
Here is a reading roadmap of Deep Learning papers! The roadmap is constructed in accordance with the following four guidelines: From outline to detail From old to state-of-the-art from generic to specific areas focus on state-of-the-art ...
Learning a Multi-Domain Curriculum for Neural Machine Translation. paper Wei Wang, Ye Tian, Jiquan Ngiam, Yinfei Yang, Isaac Caswell, Zarana Parekh Domain: Multi-Domain Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing. paper Haoming Jiang, Chen Liang, Chong...