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Anka authored and Anka committed Jun 11, 2024
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Expand Up @@ -151,7 +151,7 @@ <h2>Papers<a href="papers.html#papers" class="anchor-section" aria-label="Anchor
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<img src="images/redteaming_sam.png">
<a href="https://doi.org/10.48550/arXiv.2404.02067">Red-Teaming Segment Anything Model</a>
<a href="https://openaccess.thecvf.com/content/CVPR2024W/AdvML/html/Jankowski_Red-Teaming_Segment_Anything_Model_CVPRW_2024_paper.html">Red-Teaming Segment Anything Model</a>
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Krzysztof Jankowski, Bartlomiej Sobieski, Mateusz Kwiatkowski, Jakub Szulc, Michal Janik, Hubert Baniecki, Przemyslaw Biecek
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Expand All @@ -172,48 +172,6 @@ <h2>Papers<a href="papers.html#papers" class="anchor-section" aria-label="Anchor
<p>Remote sensing applications require machine learning models that are reliable and robust, highlighting the importance of red teaming for uncovering flaws and biases. We introduce a novel red teaming approach for hyperspectral image analysis, specifically for soil parameter estimation in the Hyperview challenge. Utilizing SHAP for red teaming, we enhanced the top-performing model based on our findings. Additionally, we introduced a new visualization technique to improve model understanding in the hyperspectral domain.</p>
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<img src="images/mini_dalex.png" align="left">
<a href="http://dalex.drwhy.ai/">DALEX</a>
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XAI with DALEX for R and Python
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<img src="images/survex.png" align="left">
<a href="https://modeloriented.github.io/survex/">survex</a>
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Explainable machine learning in survival analysis
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<img src="images/arena.png" align="left">
<a href="https://arena.drwhy.ai/docs/">Arena</a>
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Interactive tool for the exploration and comparison of models’ explanations
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<img src="images/mini_fairmodels.png" align="left">
<a href="https://modeloriented.github.io/fairmodels/">fairmodels</a>
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Fairness with fairmodels
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<img src="images/mini_crs.png" align="left">
<a href="https://crs19.pl/">COVID-19</a>
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COVID-19 Risk Score
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<img src="images/mini_archivist.png" align="left">
<a href="https://github.com/pbiecek/archivist">archivist</a>
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Model governance with R (MLOps)
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<img src="images/advxai.png">
<a href="https://doi.org/10.1016/j.inffus.2024.102303">Adversarial attacks and defenses in explainable artificial intelligence: A survey</a>
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2 changes: 1 addition & 1 deletion docs/search_index.json
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