论文标题
可区分的隐式层
Differentiable Implicit Layers
论文作者
论文摘要
在本文中，我们引入了一种有效的反向传播方案，以实现无限制的隐式函数。这些功能由一组可学习的权重参数化，并且可能取决于某些输入。使它们完全适合神经网络中的可学习层。我们在不同的应用程序上演示了我们的方案：（i）具有隐式Euler方法的神经ODE，以及（ii）模型预测性控制中的系统识别。
In this paper, we introduce an efficient backpropagation scheme for non-constrained implicit functions. These functions are parametrized by a set of learnable weights and may optionally depend on some input; making them perfectly suitable as a learnable layer in a neural network. We demonstrate our scheme on different applications: (i) neural ODEs with the implicit Euler method, and (ii) system identification in model predictive control.
