Show me what you’re looking for: visualizing abstracted transformer attention for enhancing their local interpretability on time series data L Schwenke, M Atzmueller The International FLAIRS Conference Proceedings 34, 2021 | 18 | 2021 |
Constructing global coherence representations: Identifying interpretability and coherences of transformer attention in time series data L Schwenke, M Atzmueller 2021 IEEE 8th International Conference on Data Science and Advanced …, 2021 | 9 | 2021 |
Abstracting Local Transformer Attention for Enhancing Interpretability on Time Series Data. L Schwenke, M Atzmueller LWDA, 205-218, 2021 | 6 | 2021 |
Monitoring Android devices by using events and metadata M Schölzel, E Eren, KO Detken, L Schwenke International Journal of Computing 15 (4), 248-258, 2016 | 6 | 2016 |
Identifying Informative Nodes in Attributed Spatial Sensor Networks Using Attention for Symbolic Abstraction in a GNN-based Modeling Approach L Schwenke, S Bloemheuvel, M Atzmueller The International FLAIRS Conference Proceedings 36, 2023 | 4 | 2023 |
Using brain activity patterns to differentiate real and virtual attended targets during augmented reality scenarios LM Vortmann, L Schwenke, F Putze Information 12 (6), 226, 2021 | 3 | 2021 |
Making time series embeddings more interpretable in deep learning: Extracting higher-level features via symbolic approximation representations L Schwenke, M Atzmueller The International FLAIRS Conference Proceedings 36, 2023 | 1 | 2023 |
Real or virtual? Using brain activity patterns to differentiate attended targets during augmented reality scenarios LM Vortmann, L Schwenke, F Putze arXiv preprint arXiv:2101.05272, 2021 | 1 | 2021 |
Extracting Interpretable Local and Global Representations from Attention on Time Series L Schwenke, M Atzmueller arXiv preprint arXiv:2312.11466, 2023 | | 2023 |
Knowledge-Augmented Induction of Complex Networks on Supply–Demand–Material Data D Hudson, L Schwenke, S Bloemheuvel, AG Chowdhury, N Schut, ... CEUR Workshop Proceedings, 2021 | | 2021 |