用对角概念控制循环神经网络
Controlling Recurrent Neural Networks by Diagonal Conceptors
摘要：
人类大脑能够学习、记忆和再生一整套时间模式。一种称为概念器的神经动力学机制提供了一种控制递归神经网络动态的方法，通过这种方法可以学习和回忆各种时间模式。然而，概念器是矩阵，其大小与递归神经网络中的神经元数量成二次方，因此它们很快就变得不切实际。在本论文的工作中，引入了一种概念变量，称为对角概念变量，它是对角矩阵，从而大大降低了计算成本。它将被证明，对角线概念达到相同的准确性概念，但稍微更不稳定。这种不稳定性可以改善，但需要进一步的研究。尽管如此，对角线概念是一个有前途的实际替代标准的全矩阵概念。
英文摘要：
The human brain is capable of learning, memorizing, and regenerating a panoply of temporal patterns. A neuro-dynamical mechanism called conceptors offers a method for controlling the dynamics of a recurrent neural network by which a variety of temporal patterns can be learned and recalled. However, conceptors are matrices whose size scales quadratically with the number of neurons in the recurrent neural network, hence they quickly become impractical. In the work reported in this thesis, a variation of conceptors is introduced, called diagonal conceptors, which are diagonal matrices, thus reducing the computational cost drastically. It will be shown that diagonal conceptors achieve the same accuracy as conceptors, but are slightly more unstable. This instability can be improved, but requires further research. Nevertheless, diagonal conceptors show to be a promising practical alternative to the standard full matrix conceptors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
