Nowcasting Checkpoints Improves Population Pruning in Neural Network Training

Summary

Researchers propose nowcasters that predict test accuracy from intermediate training checkpoints, using weight statistics for five-epoch forecasts and SANE transformer autoencoder representations for final accuracy. In Survival-of-the-Fittest (SOTF) training, pruning runs by nowcasted final accuracy nearly matches the best model in heterogeneous ResNet-18 and ViT model zoos, where pruning by current accuracy alone falls short.

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Why it matters by industry

• Technology

  This research introduces a reproducible method for reducing computational cost in neural network training by pruning unpromising runs early, with demonstrated gains in heterogeneous model zoos. It is relevant to software and computing infrastructure as it addresses efficient hyperparameter and architecture search.