Model-Level Dual Learningproceedings.mlr.press/v80/xia18a/xia18a.pdf Multi-Agent Dual Learning...
Method and device for classifying data using dual machine learning modelAccording to an embodiment of the present invention, a method for classifying data by using a dual machine-learning model, which is created by using a first data set selected from data of a first type and second and third...
Yiren Wang, Yingce Xia, Tianyu He, Fei Tian, Tao Qin, ChengXiang Zhai, Tie-Yan Liu, Multi-Agent Dual Learning (opens in new tab), ICLR 2019. Yingce Xia, Xu Tan, Fei Tian, Tao Qin, Nenghai Yu, and Tie-Yan Liu, Model-Level Dual Learning (opens in new tab), ICML 2018. Hany Ha...
Model Number CWHT G105X(X20)-MTK6762 Product name 10.1 Inch Android Tablet Network 4G LTE/ WCDMA/GMS OS Android 11.0 GMS 64bit(GMS Certificate Passed) Camera Front 5.0MP FF Rear 13.0MP AF RAM/ROM 2GB+32GB/3GB+64GB / 4GB+64GB/6GB+128GB ...
the learning model. Third, in certain real PLANs, the reliability of acquired labels may be doubtful. Thus, while category labels can provide positive supervision information for embedding learning process to a certain extent, in many real-world environments, the role of the supervision information ...
11. Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices 会议:NeurIPS 2019. 作者:Vincent Chen, Sen Wu, Alexander J. Ratner, Jen Weng, Christopher Ré 链接:https://papers.nips.cc/paper/9137-slice-based-learning-a-programming-model-for-residual-learning-in-cri...
In the dual-learning mechanism, we use one agent to represent the model for the primal task and the other agent to represent the model for the dual task, then ask them to teach each other through a reinforcement learning process. Based on the feedback signals generated during this process ...
Model 对偶任务:回复和问题生成 前向反向传播:使用强化学习训练 Rewards:尽可能同时提高回复内容一致性和情感表达能力 内容一致性通过重构概率表示;情感表达能力:显式和隐式情感,分别使用情感词数量和情感分类准确率作为反馈。 模型结构:红色部分输入为query和emotion(所要控制的生成的回复情感),经过前向传播模型,生成回...
吴郦军 微软亚洲研究院和中山大学联合培养博士生刚才的报告提到CycleGAN,其实是受到2016年我们在NIPS上发表的一篇Paper:Dual Learning for Machine Translation的启发,这在CycleGAN的原文中也有提到;刚才还有提到推敲的思想,这也是2017年我们在NIPS上发的文章:Deliberation Networks: SequenceGeneration Beyond One-Pass Decodin...
作者构造了两个机器阅读理解问题(machine reading comprehension,MRC),通过对两种BERT-MRC模型进行参数共享联合训练,解决了所有的子任务。作者在这些子任务上都进行了实验,几个基准数据集上的结果表明作者提出的结构的效果明显优于现有的SOTA模型。 介绍 在例句”The ambience was nice, but the service was not so ...