In this paper, we propose a deep reinforcement learning algorithm for active learning on medical image data. Although deep learning has achieved great success on medical image processing, it relies on a large number of labeled data for training, which is expensive and time-consuming. Active ...
2.2.5 Reinforcement Learning: 这里用的就是 DQN 算法,具体可以参考其他博客。 2.3 Cross-lingual policy transfer: 这里进行跨语言策略迁移的目的是:为了处理数据量比较少的语言中的 active learning 问题。作者采用 Transfer learning 的方法,在数据量丰富的数据集上学习一个比较好的 policy,然后将这种策略应用到 数...
本文罗列了最近放出来的关于深度强化学习(Deep Reinforcement Learning,DRL)的一些论文。文章采用人工定义的方式来进行组织,按照时间的先后进行排序,越新的论文,排在越前面。希望对大家有用,同时欢迎大家提…
2.2.5 Reinforcement Learning: 这里用的就是 DQN 算法,具体可以参考其他博客。 2.3 Cross-lingual policy transfer: 这里进行跨语言策略迁移的目的是:为了处理数据量比较少的语言中的 active learning 问题。作者采用 Transfer learning 的方法,在数据量丰富的数据集上学习一个比较好的 policy,然后将这种策略应用到 数...
基于DeepQNetwork algorithm,作者将奖励函数和增强学习(reinforcement learning setting)结合来学习一个定位策略(localization policy)。作者的结果表明,一个训练的agent可以在11步左右定位到一个物体的示例,这意味着该算法可以在处理11个区域之后准确的找到一个物体。
Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has been developed by the use of Deep Neural Networks (DNNs) as a potential approach to solve high-dimensional and continuous control issues effectively. Deep Reinforcement Learning techniques provide great potential in IoT, edge and SDN scenarios and are used in ...
We model an active learning algorithm as a deep neural network that inputs the base learner state and the unlabelled point set and predicts the best point to annotate next. Training this active query policy network with reinforcement learning, produces the best non-myopic policy for a given ...
Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works ha
综合大多数人对Supervised learning看了太多数据的诟病，Unsupervised 和 Reinforcement Learning，应该是一个...