论文标题
四元回向传播
Quaternion Backpropagation
论文作者
论文摘要
四季度价值的神经网络在过去几年中引起了研究人员的普及和兴趣，因此，相对于真实和想象中的部分，计算出优化所需的四元素的衍生物。但是，我们可以证明产品和链条规则不具有这种方法。我们通过采用Ghrcalculus并基于此来得出四元反向传播来解决这一问题。此外，我们在实验上证明了衍生的四元反向传播的功能。
Quaternion valued neural networks experienced rising popularity and interest from researchers in the last years, whereby the derivatives with respect to quaternions needed for optimization are calculated as the sum of the partial derivatives with respect to the real and imaginary parts. However, we can show that product- and chain-rule does not hold with this approach. We solve this by employing the GHRCalculus and derive quaternion backpropagation based on this. Furthermore, we experimentally prove the functionality of the derived quaternion backpropagation.
