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View article: REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability
REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability Open
Incorporating uncertainty is crucial to provide trustworthy explanations of deep learning models. Recent works have demonstrated how uncertainty modeling can be particularly important in the unsupervised field of representation learning ex…
View article: REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability
REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability Open
Incorporating uncertainty is crucial to provide trustworthy explanations of deep learning models. Recent works have demonstrated how uncertainty modeling can be particularly important in the unsupervised field of representation learning ex…
View article: FLEXtime: Filterbank learning to explain time series
FLEXtime: Filterbank learning to explain time series Open
State-of-the-art methods for explaining predictions from time series involve learning an instance-wise saliency mask for each time step; however, many types of time series are difficult to interpret in the time domain, due to the inherentl…
View article: Contrastive random lead coding for channel-agnostic self-supervision of biosignals
Contrastive random lead coding for channel-agnostic self-supervision of biosignals Open
Contrastive learning yields impressive results for self-supervision in computer vision. The approach relies on the creation of positive pairs, something which is often achieved through augmentations. However, for multivariate time series e…
View article: FreqRISE: Explaining time series using frequency masking
FreqRISE: Explaining time series using frequency masking Open
Time-series data are fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision making. To develop explainable artificial intelligence in t…
View article: LiRA-CD: An open-source dataset for road condition modelling and research
LiRA-CD: An open-source dataset for road condition modelling and research Open
This data article presents the details of the Live Road Assessment Custom Dataset (LiRA-CD), an open-source dataset for road condition modelling and research. The dataset captures GPS trajectories of a fleet of electric vehicles and their …
View article: Multi-view self-supervised learning for multivariate variable-channel time series
Multi-view self-supervised learning for multivariate variable-channel time series Open
Labeling of multivariate biomedical time series data is a laborious and expensive process. Self-supervised contrastive learning alleviates the need for large, labeled datasets through pretraining on unlabeled data. However, for multivariat…
View article: On convex decision regions in deep network representations
On convex decision regions in deep network representations Open
Current work on human-machine alignment aims at understanding machine-learned latent spaces and their correspondence to human representations. G{ä}rdenfors' conceptual spaces is a prominent framework for understanding human representations…