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_layouts/about.liquid

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The key motivation behind KGML is to improve the interpretability and generalization power of ML models, especially on out-of-sample distributions and even in the paucity of gold-standard data.
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To gain more insight into our research, have a look at this NeurIPS talk given by Anuj Karpatne where he discusses how integrating physics with deep learning can enhance scientific problem-solving, highlighting the limitations of black box methods.
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</p>
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<iframe width="100%" height="515" src="https://www.youtube.com/embed/POsGBquHFaY" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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<iframe width="100%" height="515" src="https://www.youtube.com/embed/5bFEnWXCAfY" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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</div>
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</div>
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<!-- Projects -->
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<!-- Projects
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<h2>
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<a href="{{ '/projects/' | relative_url }}" style="color: inherit">Projects</a>
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</h2>
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</div>
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{% endif %}
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{% endif %}
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</div> -->
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<!-- Social -->
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{% if page.social %}

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