| A Classification of Anomaly Explanation Methods
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2021 |
Tchaghe, Véronne Yepmo |
| Bringing a Ruler Into the Black Box: Uncovering Feature Impact from Individual Conditional Expectation Plots
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2021 |
Yeh, Andrew |
| Reject and Cascade Classifier with Subgroup Discovery for Interpretable Metagenomic Signatures
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2021 |
Queyrel, Maxence |
| Adversarial Generation of Temporal Data: A Critique on Fidelity of Synthetic Data
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2021 |
Debnath, Ankur |
| Ultra-low Power Machinery Fault Detection Using Deep Neural Networks
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2021 |
Nitzsche, Sven |
| Neural Maximum Independent Set
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2021 |
Pontoizeau, Thomas |
| Towards Addressing Noise and Static Variations of Analog Computations Using Efficient Retraining
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2021 |
Klein, Bernhard |
| How to Choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice
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2021 |
Vermeire, Tom |
| Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data
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2021 |
Chen, Zhi |
| Explanations for Network Embedding-Based Link Predictions
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2021 |
Kang, Bo |
| Towards Explainable Meta-learning
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2021 |
Woźnica, Katarzyna |
| Robustness of Fairness: An Experimental Analysis
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2021 |
Kamp, Serafina |
| Rule Learning Through Active Inductive Inference
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2021 |
Erdmann, Tore |
| Active Inference for Stochastic Control
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2021 |
Paul, Aswin |
| Desiderata for Explainable AI in Statistical Production Systems of the European Central Bank
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2021 |
Navarro, Carlos Mougan |
| Inferring in Circles: Active Inference in Continuous State Space Using Hierarchical Gaussian Filtering of Sufficient Statistics
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2021 |
Waade, Peter Thestrup |
| Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem
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2021 |
van Hoeffelen, N. T. A. |
| TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models
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2021 |
Schlegel, Udo |
| The Effects of Randomness on the Stability of Node Embeddings
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2021 |
Schumacher, Tobias |
| Differentially Private Learning from Label Proportions
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2021 |
Sachweh, Timon |
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