How does the scaling of synthetic pretraining data volume influence the convergence rate and final validation

Abstract

Abstract: Evidential deep learning, built upon belief theory and subjective logic, offers a principled and computationally efficient way to turn a deterministic neural network uncertainty-aware. The resultant evidential models can quantify fine-grained uncertainty using the learned evidence. To ensure theoretically sound evidential models, the evidence needs to be non-negative, which requires special activation functions for model training and inference. This constraint often leads to inferior predictive performance compared to standard softmax models, making it challenging to extend them to many large-

Research Question

How does the scaling of synthetic pretraining data volume influence the convergence rate and final validation loss of tabular foundation models compared to real-data baselines?

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