Deploying to gh-pages from @ b78249e 🚀

Author: naveenguptaiitr

assets/jupyter/blog.ipynb.html

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-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.3">Jekyll</generator><link href="https://kgml-lab.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://kgml-lab.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2025-04-20T20:00:51+00:00</updated><id>https://kgml-lab.github.io/feed.xml</id><title type="html">KGML Lab</title><subtitle>Group website of Knowledge-guided Machine Learning (KGML) Lab at Virginia Tech </subtitle><entry><title type="html">Google Gemini updates: Flash 1.5, Gemma 2 and Project Astra</title><link href="https://kgml-lab.github.io/blog/2024/google-gemini-updates-flash-15-gemma-2-and-project-astra/" rel="alternate" type="text/html" title="Google Gemini updates: Flash 1.5, Gemma 2 and Project Astra"/><published>2024-05-14T00:00:00+00:00</published><updated>2024-05-14T00:00:00+00:00</updated><id>https://kgml-lab.github.io/blog/2024/google-gemini-updates-flash-15-gemma-2-and-project-astra</id><content type="html" xml:base="https://kgml-lab.github.io/blog/2024/google-gemini-updates-flash-15-gemma-2-and-project-astra/"><![CDATA[]]></content><author><name></name></author><summary type="html"><![CDATA[We’re sharing updates across our Gemini family of models and a glimpse of Project Astra, our vision for the future of AI assistants.]]></summary></entry><entry><title type="html">Displaying External Posts on Your al-folio Blog</title><link href="https://kgml-lab.github.io/blog/2022/displaying-external-posts-on-your-al-folio-blog/" rel="alternate" type="text/html" title="Displaying External Posts on Your al-folio Blog"/><published>2022-04-23T23:20:09+00:00</published><updated>2022-04-23T23:20:09+00:00</updated><id>https://kgml-lab.github.io/blog/2022/displaying-external-posts-on-your-al-folio-blog</id><content type="html" xml:base="https://kgml-lab.github.io/blog/2022/displaying-external-posts-on-your-al-folio-blog/"><![CDATA[]]></content><author><name></name></author></entry></feed>
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feed.xml

@@ -85,7 +85,7 @@ <h2 class="subtitle is-4 has-text-weight-bold">ICLR 2025</h2>
 
 <!-- Abstract -->
 <section class="section">
-  <div class="container is-max-desktop has-text-centered">
+  <div class="container is-max-desktop has-text-justified">
     <h2 class="title is-3"> Abstract </h2>
       <p>
         In subsurface imaging, learning the mapping from velocity maps to seismic waveforms (forward problem) and waveforms to velocity (inverse problem) is important for several applications. While traditional  techniques for solving forward and inverse problems are computationally prohibitive, there is a growing interest in leveraging recent advances in deep learning to learn the mapping between velocity maps and seismic waveform images directly from data.
@@ -100,7 +100,7 @@ <h2 class="title is-3"> Abstract </h2>
 </section>
 
 <section class="section">
-  <div class="container is-max-desktop has-text-centered">
+  <div class="container is-max-desktop has-text-justified">
     <h2 class="title is-3">Method Overview</h2>
     <p>
     We propose Generalized Forward-Inverse (GFI) framework based on two assumptions. First, according to the manifold assumption, we assume that the velocity maps v ∈ V and seismic

projects/GFI-framework/index.html

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