论文标题
一种自动差异复杂损失函数的方案
A scheme for automatic differentiation of complex loss functions
论文作者
论文摘要
对于实际功能，自动差异是一种标准算法，用于有效计算其梯度，以至于它集成在各种神经网络框架中。但是，尽管在机器学习中使用复杂功能以及自动分化的完善实用性方面取得了进步，但自动分化对复杂函数的支持并不像真实功能那样建立和广泛。在这项工作中，我们提出了一种有效且无缝的方案，以实现复杂函数的自动分化，这是对当前实际功能方案的兼容概括。该方案可以大大简化使用复数的神经网络的实现。
For a real function, automatic differentiation is such a standard algorithm used to efficiently compute its gradient, that it is integrated in various neural network frameworks. However, despite the recent advances in using complex functions in machine learning and the well-established usefulness of automatic differentiation, the support of automatic differentiation for complex functions is not as well-established and widespread as for real functions. In this work we propose an efficient and seamless scheme to implement automatic differentiation for complex functions, which is a compatible generalization of the current scheme for real functions. This scheme can significantly simplify the implementation of neural networks which use complex numbers.
