8 accepted papers
The ability to properly benchmark model performance in the face of spurious correlations is important to both build better predictors and increase confidence that models are operating as intended. We demonstrate that characterizing (as opposed to simply quantifying) model mistakes across subgroups i…
Transformers were originally proposed as a sequence-to-sequence model for text but have become vital for a wide range of modalities, including images, audio, video, and undirected graphs. However, transformers for directed graphs are a surprisingly underexplored topic, despite their applicability to…
Robustness to distribution shifts is critical for deploying machine learning models in the real world. Despite this necessity, there has been little work in defining the underlying mechanisms that cause these shifts and evaluating the robustness of algorithms across multiple, different distribution…
Modern deep learning based classifiers show very high accuracy on test data but this does not provide sufficient guarantees for safe deployment, especially in high-stake AI applications such as medical diagnosis. Usually, predictions are obtained without a reliable uncertainty estimate or a formal g…
Most musical phenomena involve repetitive structures that enable listeners to track meter, i.e. the tactus or beat, the longer over-arching measure or bar, and possibly other related layers. Meters with long measure duration, sometimes lasting more than a minute, occur in many music cultures, e.g. f…
Model selection is a central topic in Bayesian machine learning, which requires the estimation of the marginal likelihood of the data under the models to be compared. During the last decade, conventional model selection methods have lost their charm as they have high computational requirements. In t…
Coupled tensor factorization methods are useful for sensor fusion, combining information from several related datasets by simultaneously approximating them by products of latent tensors. In these methods, the choice of a suitable optimization criteria becomes difficult as observed datasets may have…
Section linking aims at relating structural units in the notation of a piece of music to their occurrences in a performance of the piece. In this paper, we address this task by presenting a score-informed hierarchical Hidden Markov Model (HHMM) for modeling musical audio signals on the temporal leve…