Saved in:
| Main Authors: | Knab, Patrick, Marton, Sascha, Bartelt, Christian, Fuder, Robert |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.01713 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Which LIME should I trust? Concepts, Challenges, and Solutions
by: Knab, Patrick, et al.
Published: (2025)
by: Knab, Patrick, et al.
Published: (2025)
Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation Models
by: Knab, Patrick, et al.
Published: (2024)
by: Knab, Patrick, et al.
Published: (2024)
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data
by: Tschalzev, Andrej, et al.
Published: (2024)
by: Tschalzev, Andrej, et al.
Published: (2024)
Explaining Neural Networks without Access to Training Data
by: Marton, Sascha, et al.
Published: (2022)
by: Marton, Sascha, et al.
Published: (2022)
GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
by: Marton, Sascha, et al.
Published: (2023)
by: Marton, Sascha, et al.
Published: (2023)
Disentangling Exploration of Large Language Models by Optimal Exploitation
by: Grams, Tim, et al.
Published: (2025)
by: Grams, Tim, et al.
Published: (2025)
GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
by: Marton, Sascha, et al.
Published: (2023)
by: Marton, Sascha, et al.
Published: (2023)
Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics
by: Santamaria-Valenzuela, Inmaculada, et al.
Published: (2025)
by: Santamaria-Valenzuela, Inmaculada, et al.
Published: (2025)
PHEATPRUNER: Interpretable Data-centric Feature Selection for Multivariate Time Series Classification through Persistent Homology
by: Pham, Anh-Duy, et al.
Published: (2025)
by: Pham, Anh-Duy, et al.
Published: (2025)
Heteroscedastic Temporal Variational Autoencoder For Irregular Time Series
by: Shukla, Satya Narayan, et al.
Published: (2021)
by: Shukla, Satya Narayan, et al.
Published: (2021)
Beyond Either-Or Reasoning: Transduction and Induction as Cooperative Problem-Solving Paradigms
by: Zenkner, Janis, et al.
Published: (2025)
by: Zenkner, Janis, et al.
Published: (2025)
Concepts in Motion: Temporal Concept Bottleneck Model for Interpretable Video Classification
by: Knab, Patrick, et al.
Published: (2025)
by: Knab, Patrick, et al.
Published: (2025)
DCBM: Data-Efficient Visual Concept Bottleneck Models
by: Prasse, Katharina, et al.
Published: (2024)
by: Prasse, Katharina, et al.
Published: (2024)
Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit
by: Jiang, Nick, et al.
Published: (2025)
by: Jiang, Nick, et al.
Published: (2025)
AVATAR: Adversarial Autoencoders with Autoregressive Refinement for Time Series Generation
by: EskandariNasab, MohammadReza, et al.
Published: (2025)
by: EskandariNasab, MohammadReza, et al.
Published: (2025)
Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining
by: Yan, Xu, et al.
Published: (2024)
by: Yan, Xu, et al.
Published: (2024)
Decoding Dark Matter: Specialized Sparse Autoencoders for Interpreting Rare Concepts in Foundation Models
by: Muhamed, Aashiq, et al.
Published: (2024)
by: Muhamed, Aashiq, et al.
Published: (2024)
Correlating Time Series with Interpretable Convolutional Kernels
by: Chen, Xinyu, et al.
Published: (2024)
by: Chen, Xinyu, et al.
Published: (2024)
Interpretable Time Series Autoregression for Periodicity Quantification
by: Chen, Xinyu, et al.
Published: (2025)
by: Chen, Xinyu, et al.
Published: (2025)
Clustering Time Series Data with Gaussian Mixture Embeddings in a Graph Autoencoder Framework
by: Afzali, Amirabbas, et al.
Published: (2024)
by: Afzali, Amirabbas, et al.
Published: (2024)
Interpretable Pre-Trained Transformers for Heart Time-Series Data
by: Davies, Harry J., et al.
Published: (2024)
by: Davies, Harry J., et al.
Published: (2024)
TimePerceiver: An Encoder-Decoder Framework for Generalized Time-Series Forecasting
by: Lee, Jaebin, et al.
Published: (2025)
by: Lee, Jaebin, et al.
Published: (2025)
A Unified Frequency Domain Decomposition Framework for Interpretable and Robust Time Series Forecasting
by: He, Cheng, et al.
Published: (2025)
by: He, Cheng, et al.
Published: (2025)
Unsupervised Event Outlier Detection in Continuous Time
by: Nath, Somjit, et al.
Published: (2024)
by: Nath, Somjit, et al.
Published: (2024)
Rethinking Evaluation in the Era of Time Series Foundation Models: (Un)known Information Leakage Challenges
by: Meyer, Marcel, et al.
Published: (2025)
by: Meyer, Marcel, et al.
Published: (2025)
ST-Tree with Interpretability for Multivariate Time Series Classification
by: Du, Mingsen, et al.
Published: (2024)
by: Du, Mingsen, et al.
Published: (2024)
Evaluating Simplification Algorithms for Interpretability of Time Series Classification
by: Håvardstun, Brigt, et al.
Published: (2025)
by: Håvardstun, Brigt, et al.
Published: (2025)
CRITS: Convolutional Rectifier for Interpretable Time Series Classification
by: Kuratomi, Alejandro, et al.
Published: (2025)
by: Kuratomi, Alejandro, et al.
Published: (2025)
Mechanistic Interpretability for Transformer-based Time Series Classification
by: Kalnāre, Matīss, et al.
Published: (2025)
by: Kalnāre, Matīss, et al.
Published: (2025)
IRDS: Interpretable RLVR Data Selection via Verifier-Coupled Sparse Autoencoder Coverage
by: Li, Yuhan, et al.
Published: (2026)
by: Li, Yuhan, et al.
Published: (2026)
Explanation Space: A New Perspective into Time Series Interpretability
by: Rezaei, Shahbaz, et al.
Published: (2024)
by: Rezaei, Shahbaz, et al.
Published: (2024)
Diffusion-TS: Interpretable Diffusion for General Time Series Generation
by: Yuan, Xinyu, et al.
Published: (2024)
by: Yuan, Xinyu, et al.
Published: (2024)
PatchDecomp: Interpretable Patch-Based Time Series Forecasting
by: Tomioka, Hiroki, et al.
Published: (2026)
by: Tomioka, Hiroki, et al.
Published: (2026)
Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers (Extended Version)
by: Röchner, Philipp, et al.
Published: (2024)
by: Röchner, Philipp, et al.
Published: (2024)
Tabular Data Adapters: Improving Outlier Detection for Unlabeled Private Data
by: Herurkar, Dayananda, et al.
Published: (2025)
by: Herurkar, Dayananda, et al.
Published: (2025)
Long-Term Outlier Prediction Through Outlier Score Modeling
by: Aoki, Yuma, et al.
Published: (2026)
by: Aoki, Yuma, et al.
Published: (2026)
Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability
by: Xu, Kunpeng, et al.
Published: (2024)
by: Xu, Kunpeng, et al.
Published: (2024)
Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows
by: Chiaburu, Teodor, et al.
Published: (2024)
by: Chiaburu, Teodor, et al.
Published: (2024)
Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification
by: Kan, Ziwen, et al.
Published: (2024)
by: Kan, Ziwen, et al.
Published: (2024)
EDformer: Embedded Decomposition Transformer for Interpretable Multivariate Time Series Predictions
by: Chakraborty, Sanjay, et al.
Published: (2024)
by: Chakraborty, Sanjay, et al.
Published: (2024)
Similar Items
-
Which LIME should I trust? Concepts, Challenges, and Solutions
by: Knab, Patrick, et al.
Published: (2025) -
Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation Models
by: Knab, Patrick, et al.
Published: (2024) -
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data
by: Tschalzev, Andrej, et al.
Published: (2024) -
Explaining Neural Networks without Access to Training Data
by: Marton, Sascha, et al.
Published: (2022) -
GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
by: Marton, Sascha, et al.
Published: (2023)