Software

Act-Bench datasets provide training results for 2,913 activation functions, enabling quick benchmarking of new AutoML algorithms.

AutoInit optimizes deep learning initialization for robust learning and integrates with TensorFlow experiments.

TaylorGLO evolves loss functions with multivariate Taylor polynomials for automatic regularization.

The Traveling Observer Model (TOM) implements deep multi-task learning through spatial variable embeddings.

Residual-based error detection (RED) extends RIO to classification, identifying misclassifications, out-of-distribution, and adversarial inputs.

This package provides predictors, prescriptors, and evaluation code for forecasting COVID-19 cases and interventions.

Residual estimation with input/output kernels (RIO) quantifies confidence and enhances point-prediction accuracy.

Modular Universal Reparameterization (MUiR) enables multitask learning across language, vision, and genetics.