Categorical Foundations of Gradient-Based Learning

Categorical Foundations of Gradient-Based Learning

We propose a categorical semantics of gradient-based machine learning algorithms in terms of lens...概要を表示 We propose a categorical semantics of gradient-based machine learning algorithms in terms of lenses, parametrised maps, and reverse derivative categories. This foundation provides a powerful explanatory and unifying framework: it encompasses a variety of gradient descent algorithms such as ADAM, AdaGrad, and Nesterov momentum, as well as a variety of loss functions such as as MSE and Softmax cross

• 機械学習
• 数学