Continuous-Time Meta-Learning with Forward Mode Differentiation

@article{Deleu2022ContinuousTimeMW,
  title={Continuous-Time Meta-Learning with Forward Mode Differentiation},
  author={Tristan Deleu and David Kanaa and Leo Feng and Giancarlo Kerg and Yoshua Bengio and Guillaume Lajoie and Pierre-Luc Bacon},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.01443}
}
Drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introduce Continuous-Time Meta-Learning (COMLN), a meta-learning algorithm where adaptation follows the dynamics of a gradient vector field. Specifically, representations of the inputs are meta-learned such that a taskspecific linear classifier is obtained as a solution of an ordinary differential equation (ODE). Treating the learning process as an ODE offers the notable advantage that the… 

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