Learning to stop while learning to predict

ICML2020 9-25-2020

Motivation

Task-imbalanced meta learning: different tasks need different numbers of gradient steps for adaptation
Deep learning based algorithms usually have a fixed number of iterations in the architecture.

could we learn to stop automatically?

predictive model $\mathcal{F}_\theta$ : transforms the input x to generate a path of states $x_1, x_2, ...,x_T$
stop policy $\pi_\phi$ : sequentially observes the states $x_t$ and determines the probability of stop at layer $t$
variational stop time distribution $q_\phi$ : stop time distribution induced by stopping policy $\pi_\phi$

Last updated 4 years ago

Overview

predictive model $\mathcal{F}_\theta$ : transforms the input x to generate a path of states $x_1, x_2, ...,x_T$

stop policy $\pi_\phi$ : sequentially observes the states $x_t$ and determines the probability of stop at layer $t$

variational stop time distribution $q_\phi$ : stop time distribution induced by stopping policy $\pi_\phi$