Abstract: Continual learning (CL) algorithms seek to train a model when faced with similar tasks observed in a sequential manner.
Aug 5, 2020 · We develop a new theoretical approach for meta continual learning~(MCL) where we mathematically model the learning dynamics using dynamic programming.
We present a new theoretical framework that models the learning dynamics in CL through dynamic programming.
Abstract—Continual learning (CL) algorithms seek to train a model when faced with similar tasks observed in a sequen- tial manner.
Meta continual learning algorithms seek to train a model when faced with sim- ilar tasks observed in a sequential manner. Despite promising methodological.
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The model uses backward dynamic programming to find the optimum solution. Our approach is unique since it is the first bidding model that considers impact of ...
A new theoretical approach is developed for meta continual learning~(MCL) where it is shown that the theoretically grounded method achieves accuracy better ...
We formulate the continual learning problem via dynamic programming and model the trade-off between catastrophic forgetting and generalization as a two ...
Sep 28, 2021 · We formulate the continual learning (CL) problem via dynamic programming and model the trade-off between catastrophic forgetting and generalization as a two- ...
We formulate the continual learning (CL) problem via dynamic programming and model the trade-off between catastrophic forgetting and generalization as a ...