论文标题
跨熵作为音乐生成模型的目标功能
Cross entropy as objective function for music generative models
论文作者
论文摘要
在训练机器学习模型时优化功能的选举非常重要，因为这使该模型可以学习。这不是很小的，因为有很多选择，每个选项都出于不同的目的。在文本序列生成的情况下，横熵是一个常见的选择，因为它有能力量化模型的预测行为。在本文中，我们通过实验测试了音乐发生器模型的跨熵的有效性，该实验旨在将损耗值的改善与降低随机性和保持一致的旋律的能力相关联。我们还分析了这两个方面之间的关系，这些方面分别与短期和长期记忆以及它们的行为方式和学习方式不同。
The election of the function to optimize when training a machine learning model is very important since this is which lets the model learn. It is not trivial since there are many options, each for different purposes. In the case of sequence generation of text, cross entropy is a common option because of its capability to quantify the predictive behavior of the model. In this paper, we test the validity of cross entropy for a music generator model with an experiment that aims to correlate improvements in the loss value with the reduction of randomness and the ability to keep consistent melodies. We also analyze the relationship between these two aspects which respectively relate to short and long term memory and how they behave and are learned differently.
