基于深度学习的推荐系统研究综述 - ict.ac.cn

(Beijing Institute of Remote Sensing, Beijing . 100192) 2) (D. epartment of Computer Science and Technology, Tsinghua University, Beijing 100084) Abstract. With the ever-growing volume, complexity and dynamicity of online information, recommender

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脉冲神经网络研究现状及展望 - ict.ac.cn

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(e.g., dopamine-based reward learning, energy-based learning). Hence, it is powerful on spatially-temporal information representation, asynchronous processing of event-based information, and self-organized learning with dynamic topologies. SNN belongs to cross-discipline research areas of brain science and computer science.

  Temporal

数据中心网络的流量控制：研究现状与趋势

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DCTCP, and the most suitable traffic control algorithm for RDMA data center is DCQCN. Other researches require expensive custom hardware, which is difficult to deploy. (2) The traffic control technology is a technology of fair utilization of limited resources. Therefore, the performance of the technology can be improved

  Dctcp

区块链技术：架构及进展 - ict.ac.cn

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blockchain scalability is analyzed about sharding and multichannel, blockchain security is discussed from digital signing and verification, and privacy preserving. This paper also analyzes the advantages, disadvantages and technology trends of blockchain by comparing with traditional databases, and gives several challenging research

  Security, Scalability

车联网边缘计算环境下基于深度强化学习的分布 式服务卸载方法

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A Deep Reinforcement Learning-BasedDistributed Service Offloading Method ... average service latency by 0.4% to 20.4% compared with four exiting service offloading methods in different IoV environments, proving the effectiveness and efficiency of D-SOAC. ... asynchronous advantage actor-critic 1 引言 ...

  Methods, Deep, Reinforcement, Asynchronous, Deep reinforcement

深度强化学习综述 - ict.ac.cn

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optimization methods to optimize the policies. In this part, we firstly highlight some pure policy gradient methods, then focus on a series of policy-based DRL algorithms which use the actor-critic framework e.g., Deep Deterministic Policy Gradient (DDPG), followed by an effective method named Asynchronous Advantage

  Methods, Deep, Asynchronous

卷积神经网络研究综述 - ict.ac.cn

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AlphaGo 主要采用价值网络（value networks） 来评估棋盘的位置，用策略网络（policy networks） 来选择下棋步法，这两种网络都是深层神经网络模 型，AlphaGo 所取得的成果是深度学习带来的人工 智能的又一次突破，这也说明了深度学习具有强大 的潜力。

  Alphago

生成对抗网络及其在图像生成中的 ... - ict.ac.cn

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and computational challenges. At the same time, generative adversarial networks are the latest and most successful technology among generative models. Especially in terms of image generation, compared with other generation models, generative adversarial networks can not only avoid complicated calculations, but also generate better quality images.

  Adversarial, Generative, Generative adversarial

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