Wf是参数矩阵,gt是fusion gate,用来从external memory选择信息,通过如下计算: fusion gate 最终:通过如下方式更新external memory enhanced LSTM: 更新 参数p代表LSTM所有内部的参数,参数q代表所有外部记忆的参数。 Deep Architectures with shared memory for multi-task learning: 现有的单任务学习都收到训练数据有限的...
This study presents a new approach of using multitask learning deep neural network (MTLDNN) to combine RP and SP data and incorporate the traditional nest logit approach as a special case. Based on a combined RP and SP survey in Singapore to examine the demand for autonomous vehicles (AV),...
Inspired by this, we propose a deep-learning multitask model of exercise recognition and repetition counting. To the best of our knowledge, this approach is attempted for the first time. To meet the needs of the multitask model, we create a new dataset Rep-Penn with action, counting and ...
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 ...
In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-
multi-task-L-UNet -> A Deep Multi-Task Learning Framework Coupling Semantic Segmentation and Fully Convolutional LSTM Networks for Urban Change Detection. Applied to SpaceNet7 dataset urban_change_detection -> Detecting Urban Changes With Recurrent Neural Networks From Multitemporal Sentinel-2 Data. fa...
1. This linear nature of multi-view data make the learning task on multi-view data remain still challenging. Recently, due to the powerful feature abstraction ability, deep learning methods [31] have vast inroads into many applications with outstanding performance, such as computer vision [20],...
Illustration of multi-task deep learning and multi-task D2NN architecture with two image classification tasks deployed. The proposed multi-task D2NN architecture is formed by four shared diffractive layers and two multi-task layers, where the feed-forward computations have been re-used into multi...
aThe pipeline of RetroExplainer. We formulated the whole process as four distinct phases: (1) molecular graph encoding, (2) multi-task learning, (3) decision-making, and (4) prediction or multi-step pathway planning.bThe architecture of the multi-sense and multi-scale Graph Transformer (MSMS...
Multi-task Self-Supervised Visual Learning [arXiv] Learning a Multi-View Stereo Machine [arXiv] [article] [code] (soon) Twin Networks: Using the Future as a Regularizer [arXiv] A Brief Survey of Deep Reinforcement Learning [arXiv] Scalable trust-region method for deep reinforcement learning ...