深度强化学习综述 - ict.ac.cn
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
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基于深度学习的推荐系统研究综述 - ict.ac.cn
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卷积神经网络研究综述 - ict.ac.cn
cjc.ict.ac.cnAlphaGo 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。
生成对抗网络及其在图像生成中的 ... - ict.ac.cn
cjc.ict.ac.cnand 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.
脉冲神经网络研究现状及展望 - ict.ac.cn
cjc.ict.ac.cn(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.
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