4 accepted papers
Bayesian neural networks (BNN) promise to combine the predictive performance of neural networks with principled uncertainty modeling crucial for safety-critical systems and decision making. However, posterior uncertainties depend on the choice of prior, and finding informative priors in weight-space
Laplace approximations are popular techniques for endowing deep networks with epistemic uncertainty estimates as they can be applied without altering the predictions of the trained network, and they scale to large models and datasets. While the choice of prior strongly affects the resulting posterio…
Gradient boosting machines (GBMs) based on decision trees consistently demonstrate state-of-the-art results on regression and classification tasks with tabular data, often outperforming deep neural networks. However, these models do not provide well-calibrated predictive uncertainties, which prevent…
We present a method to transform artificial notification sounds into various musical timbres. To tackle the issues of ambiguous timbre definition, the lack of paired notification-music sample sets, and the lack of sufficient training data of notifications, we adapt the problem for a cycle-consistent…