Continuous-depth learning has recently emerged as a novel perspective on deep
learning, improving performance in tasks related to dynamical systems and
density estimation. Core to these approaches is the neural differential
equation, whose forward passes are the solutions of an initial value problem
parametrized by a neural network. Unlocking the full potential of
continuous-depth models requires a different set of software tools, due to
peculiar differences compared to standard discrete neural networks, e.g
inference must be carried out via numerical solvers. We introduce TorchDyn, a
PyTorch library dedicated to continuous-depth learning, designed to elevate
neural differential equations to be as accessible as regular plug-and-play deep
learning primitives. This objective is achieved by identifying and subdividing
different variants into common essential components, which can be combined and
freely repurposed to obtain complex compositional architectures. TorchDyn
further offers step-by-step tutorials and benchmarks designed to guide
researchers and contributors.

本文介绍了 TorchDyn，一个专为连续深度学习而设计的 PyTorch 库，旨在将神经微分方程提升为与常规深度学习元素一样易用，该库基于将不同变量标识和子分成通用基本组件，可以自由地重新组合以获得复杂的组合架构，同时 TorchDyn 还提供逐步教程和基准测试，旨在引导研究人员和贡献者。