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02/14/2023
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus
Explainable AI (XAI) is a rapidly evolving field that aims to improve tr...
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05/04/2022
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI
The ability to continuously process and retain new information like we d...
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02/14/2022
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations
The evaluation of explanation methods is a research topic that has not y...
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02/07/2022
PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging
State-of-the-art machine learning models are commonly (pre-)trained on l...
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04/22/2020
Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution
Integrated gradients as an attribution method for deep neural network mo...
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03/17/2020
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond
With the broader and highly successful usage of machine learning in indu...
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01/04/2020
Understanding Image Captioning Models beyond Visualizing Attention
This paper explains predictions of image captioning models with attentio...
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12/22/2019
Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed
Today's machine learning models for computer vision are typically traine...
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12/16/2019
On the Understanding and Interpretation of Machine Learning Predictions in Clinical Gait Analysis Using Explainable Artificial Intelligence
Systems incorporating Artificial Intelligence (AI) and machine learning ...
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07/09/2018
Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals
Interpretability of deep neural networks is a recently emerging area of ...
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08/25/2017
