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...
Deep learning has improved performance on many natural language processing (NLP) tasks individually. However, general NLP models cannot emerge within a par... B Mccann,NS Keskar,C Xiong,... 被引量: 36发表: 2018年 Effective shared representations with multitask learning for community question answ...
In particular, we summarize six perspectives from the current literature on deep multimodal learning, namely: multimodal data representation, multimodal fusion (i.e., both traditional and deep learning-based schemes), multitask learning, multimodal alignment, multimodal transfer learning, and zero-shot ...
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),...
In this paper, we propose a combination of two powerful techniques, deep learning and parallel computing, to significantly reduce the complexity of the HEVC encoding engine. Our experimental results show that a combination of deep learning to reduce the CTU partitioning complexity with parallel ...
A unified architecture for natural language processing: deep neural networks with multitask learning Proceedings of the International Conference on Machine Learning (2008), pp. 160-167 CrossrefGoogle Scholar Dahl, Yu, Deng, Acero, 2012 G.E. Dahl, D. Yu, L. Deng, A. Acero Context-dependent ...
[58]Parisotto, Emilio, Jimmy Lei Ba, and Ruslan Salakhutdinov. "Actor-mimic: Deep multitask and transfer reinforcement learning." arXiv preprint arXiv:1511.06342 (2015).[pdf](RL domain)⭐⭐⭐ [59]Rusu, Andrei A., et al. "Progressive neural networks." arXiv preprint arXiv:1606.04671 ...
Land Cover Classification with U-Net -> Satellite Image Multi-Class Semantic Segmentation Task with PyTorch Implementation of U-Net, uses DeepGlobe Land Cover Segmentation dataset, with code Multi-class semantic segmentation of satellite images using U-Net using DSTL dataset, tensorflow 1 & python 2....
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...
A long-standing challenge in brain tumor segmentation is data imbalance. To effectively deal with the imbalance problem, researchers try different solutions, such as network cascade and ensemble [64,67,130], multi-task learning [97,150], and customized loss functions [120]. Another solution is ...