Meike Nauta

Orcid: 0000-0002-0558-3810

According to our database1, Meike Nauta authored at least 17 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

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Bibliography

2024
PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans.
CoRR, 2024

2023
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI.
ACM Comput. Surv., 2023

Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN.
CoRR, 2023

The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifiers.
Proceedings of the Explainable Artificial Intelligence, 2023

Benchmarking eXplainable AI - A Survey on Available Toolkits and Open Challenges.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Interpreting and Correcting Medical Image Classification with PIP-Net.
Proceedings of the Artificial Intelligence. ECAI 2023 International Workshops - XAI³, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30, 2023

PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Feature Attribution Explanations for Spiking Neural Networks.
Proceedings of the 5th IEEE International Conference on Cognitive Machine Intelligence, 2023

2022
Radiology report generation for proximal femur fractures using deep classification and language generation models.
Artif. Intell. Medicine, 2022

2021
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition.
Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

Neural Prototype Trees for Interpretable Fine-Grained Image Recognition.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Interactive Explanations of Internal Representations of Neural Network Layers: An Exploratory Study on Outcome Prediction of Comatose Patients.
Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data co-located with 24th European Conference on Artificial Intelligence, 2020

2019
Causal Discovery with Attention-Based Convolutional Neural Networks.
Mach. Learn. Knowl. Extr., 2019

Evaluating CNN interpretability on sketch classification.
Proceedings of the Twelfth International Conference on Machine Vision, 2019

Visualising the Training Process of Convolutional Neural Networks for Non-Experts.
Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 2019

2018
LIFT: Learning Fault Trees from Observational Data.
Proceedings of the Quantitative Evaluation of Systems - 15th International Conference, 2018

2017
Detecting Hacked Twitter Accounts based on Behavioural Change.
Proceedings of the 13th International Conference on Web Information Systems and Technologies, 2017


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