Interpretation of time-series deep models: A survey
Deep learning models developed for time-series associated tasks have become more
widely researched nowadays. However, due to the unintuitive nature of time-series data, the …
widely researched nowadays. However, due to the unintuitive nature of time-series data, the …
[HTML][HTML] Understand your decision rather than your model prescription: Towards explainable deep learning approaches for commodity procurement
M Rettinger, S Minner, J Birzl - Computers & Operations Research, 2025 - Elsevier
Hedging against price increases is particularly important in times of significant market
uncertainty and price volatility. For commodity procuring firms, futures contracts are a …
uncertainty and price volatility. For commodity procuring firms, futures contracts are a …
Exploring Dataset Bias and Scaling Techniques in Multi-Source Gait Biomechanics: An Explainable Machine Learning Approach
Machine learning has become increasingly important in biomechanics. It allows to unveil
hidden patterns from large and complex data, which leads to a more comprehensive …
hidden patterns from large and complex data, which leads to a more comprehensive …
What about the Latent Space? The Need for Latent Feature Saliency Detection in Deep Time Series Classification
Saliency methods are designed to provide explainability for deep image processing models
by assigning feature-wise importance scores and thus detecting informative regions in the …
by assigning feature-wise importance scores and thus detecting informative regions in the …
Sequence-based Learning
Learning from time series data is an essential component in the AI landscape given the
ubiquitous time-dependent data in real-world applications. To motivate the necessity of …
ubiquitous time-dependent data in real-world applications. To motivate the necessity of …