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   <meta name="description" content="A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations.">
   <title>GFI: Generalized Forward-Inverse Framework</title>
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@@ -134,7 +123,7 @@ <h2 class="banded">Method Overview</h2>
     </ol>
 
   <!-- Experiments -->
-  <h2 class="banded"> Experiments </h2>
+  <h2 class="banded"> Experiments and Results </h2>
   <p style="text-align:justify;">
     We consider the OpenFWI collection of datasets, comprising multi-structural benchmark datasets for DL4SI grouped into: Vel, Fault, and Style Families. We compare Latent U-Net and Invertible X-Net on these datasets against several baseline methods for both forward and inverse problems. 
     <ol>
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       </li>
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-     
+  <h2 class="banded"> BibTeX </h2>
+  <pre class="reference">
+    <code>
+      @inproceedings{
+      gupta2025a,
+      title={A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations},
+      author={Naveen Gupta and Medha Sawhney and Arka Daw and Youzuo Lin and Anuj Karpatne},
+      booktitle={The Thirteenth International Conference on Learning Representations},
+      year={2025},
+      url={https://openreview.net/forum?id=yIlyHJdYV3}
+      }
+    </code>
+  </pre>
+
+  <h2 class="banded">Acknowledgement</h2>
+  <p>
+        This work was supported in part by NSF awards IIS-2239328 and IIS-2107332. We are grateful
+        to the Advanced Research Computing (ARC) Center at Virginia Tech for providing access to GPU
+        compute resources for this project. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government
+        retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the
+        published form of this manuscript, or allow others to do so, for US government purposes. DOE will
+        provide public access to these results of federally sponsored research in accordance with the DOE
+        Public Access Plan ( https://www.energy.gov/doe-public-access-plan).
+  </p>
 
 
 
-  
+<!--   
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@@ -194,22 +206,8 @@ <h2 class="title">BibTeX</h2>
   url={https://openreview.net/forum?id=yIlyHJdYV3}
   }</code></pre>
     </div>
-  </section>
+  </section> -->
 
-  <section class="section" id="acknowledgement">
-    <div class="container is-max-desktop content">
-      <h2 class="title">Acknowledgement</h2>
-      <p>
-        This work was supported in part by NSF awards IIS-2239328 and IIS-2107332. We are grateful
-        to the Advanced Research Computing (ARC) Center at Virginia Tech for providing access to GPU
-        compute resources for this project. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government
-        retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the
-        published form of this manuscript, or allow others to do so, for US government purposes. DOE will
-        provide public access to these results of federally sponsored research in accordance with the DOE
-        Public Access Plan ( https://www.energy.gov/doe-public-access-plan).
-      </p>
-    </div>
-  </section>
   </main>
 
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