Search Results for author:
Found 2 papers, 1 papers with code
Bayesian Neural Network Versus Ex-Post Calibration For Prediction Uncertainty
Probabilistic predictions from neural networks which account for predictive uncertainty during classification is crucial in many real-world and high-impact decision making settings.
Creating Powerful and Interpretable Models with Regression Networks
While some methods for combining these exist in the literature, our architecture generalizes these approaches by taking interactions into account, offering the power of a dense neural network without forsaking interpretability.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
