Predicting Downstream Model Performance at Early Training Stages: A New Perspective on Neural Network Selection via Edge Dynamics | Synced

A research team from Rensselaer Polytechnic Institute, Thomas J. Watson Research Center and the University of California, Los Angeles proposes a novel framework for effective pretrained neural network model selection for downstream tasks that forecasts the predictive ability of a model with its cumulative information in the early phase of neural network training.