Comparing the functional behavior of neural network models, whether it is a single network
over time or two (or more networks) during or post-training, is an essential step in …

Semantic segmentation, a fundamental task in environmental understanding, aims to assign
each image pixel to a specific class. Despite recent progress, segmentation accuracy in …

Pooling multiple neuroimaging datasets across institutions often enables significant
improvements in statistical power when evaluating associations (eg, between risk factors …

The lack of robustness is a serious problem for deep neural networks (DNNs) and makes
DNNs vulnerable to adversarial examples. A promising solution is applying adversarial …

Optimal transport has emerged as a powerful tool for a variety of problems in machine
learning, and it is frequently used to enforce distributional constraints. In this context, existing …

This technical reports describes our contribution to the DCASE challenge 2023 Acoustic
Scene Classification Task 1. We apply Inverse Contrastive Learning to regularize models …

Due to individual heterogeneity, performance gaps are observed between generic (one-size-
fits-all) models and person-specific models in data-driven health applications. However, in …

ABSTRACT Acoustic Scene Classification poses a significant challenge in the DCASE Task
1 TAU22 dataset with a sample length of only a single second. The best performing model in …