Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning.

In deep learning, achieving high performance on image classification tasks requires diverse training sets. However, the current best practice-maximizing dataset size and class balance-does not guarantee dataset diversity. We hypothesized that, for a given model architecture, model performance can be improved by maximizing diversity more directly.

Scaling Monte-Carlo-Based Inference on Antibody and TCR Repertoires.

Previously, it has been shown that maximum-entropy models of immune-repertoire sequence can be used to determine a person's vaccination status. However, this approach has the drawback of requiring a computationally intensive method to compute each model's partition function , the normalization constant required for calculating the probability that the model will generate a given sequence. Specifically, the method required generating approximately 10 sequences via Monte-Carlo simulations for each model.