深度递归神经网络的学习与合成][Learning to learn and compositionality with deep recurrent neural networks]

深度递归神经网络的学习与合成 Learning to learn and compositionality with deep recurrent neural networks


课程网址:	http://videolectures.net/kdd2016_de_freitas_recurrent_neural/
主讲教师:	Nando de Freitas
开课单位:	视频讲座网
开课时间:	2016-08-31
课程语种:	英语
中文简介:	深度神经网络表示在计算机视觉、语音、计算语言学、机器人、强化学习和许多其他数据丰富的领域发挥着重要作用。在这次演讲中，我将展示学习-学习和组合是处理知识转移的关键成分，以解决广泛的任务，处理小数据体系和持续学习。我将用三个例子来说明这一点:学习学习算法、神经编程和解释器以及学习交流。
课程简介:	Deep neural network representations play an important role in computer vision, speech, computational linguistics, robotics, reinforcement learning and many other data-rich domains. In this talk I will show that learning-to-learn and compositionality are key ingredients for dealing with knowledge transfer so as to solve a wide range of tasks, for dealing with small-data regimes, and for continual learning. I will demonstrate this with three examples: learning learning algorithms, neural programmers and interpreters, and learning communication.
关键词:	神经网络; 知识转移; 持续学习; 学习算法; 神经编程
课程来源:	视频讲座网
数据采集:	2022-11-20：chenxin01
最后编审:	2023-05-18：liyy
阅读次数:	36

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