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acemaxx<p>The investment landscape for emerging market debt <a href="https://econtwitter.net/tags/EMD" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>EMD</span></a> is characterized by domestic improvements and fundamental strength but external challenges remain primarily due to the policy mix of the new US Administration, chart @GoldmanSachs</p>
idw_online<p>Juniorgruppen @MSNZ_wuerzburg haben im @BloodJournal detaillierte Analyse von extramedullären Läsionen <a href="https://idw-online.social/tags/EMD" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>EMD</span></a> beim Multiplen <a href="https://idw-online.social/tags/Myelom" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Myelom</span></a> veröffentlicht. Sie zeigen, warum EMD so schlecht auf gängige Immuntherapien anspricht &amp; neue Therapieansätze.<br><a href="https://nachrichten.idw-online.de/2024/11/18/was-t-zellen-im-tumor-muede-macht" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">nachrichten.idw-online.de/2024</span><span class="invisible">/11/18/was-t-zellen-im-tumor-muede-macht</span></a></p>
Fabrizio Musacchio<p>The <a href="https://sigmoid.social/tags/Wasserstein" class="mention hashtag" rel="tag">#<span>Wasserstein</span></a> <a href="https://sigmoid.social/tags/metric" class="mention hashtag" rel="tag">#<span>metric</span></a> (<a href="https://sigmoid.social/tags/EMD" class="mention hashtag" rel="tag">#<span>EMD</span></a>) can be used, to train <a href="https://sigmoid.social/tags/GenerativeAdversarialNetworks" class="mention hashtag" rel="tag">#<span>GenerativeAdversarialNetworks</span></a> (<a href="https://sigmoid.social/tags/GANs" class="mention hashtag" rel="tag">#<span>GANs</span></a>) more effectively. This tutorial compares a default GAN with a <a href="https://sigmoid.social/tags/WassersteinGAN" class="mention hashtag" rel="tag">#<span>WassersteinGAN</span></a> (<a href="https://sigmoid.social/tags/WGAN" class="mention hashtag" rel="tag">#<span>WGAN</span></a>) trained on the <a href="https://sigmoid.social/tags/MNIST" class="mention hashtag" rel="tag">#<span>MNIST</span></a> dataset.</p><p>🌎 <a href="https://www.fabriziomusacchio.com/blog/2023-07-29-wgan/" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">3-07-29-wgan/</span></a></p><p><a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="tag">#<span>MachineLearning</span></a></p>
Fabrizio Musacchio<p>Apart from <a href="https://sigmoid.social/tags/Wasserstein" class="mention hashtag" rel="tag">#<span>Wasserstein</span></a> Distance (<a href="https://sigmoid.social/tags/EMD" class="mention hashtag" rel="tag">#<span>EMD</span></a>), other <a href="https://sigmoid.social/tags/metrics" class="mention hashtag" rel="tag">#<span>metrics</span></a> also play an important role in <a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="tag">#<span>MachineLearning</span></a> tasks such as <a href="https://sigmoid.social/tags/clustering" class="mention hashtag" rel="tag">#<span>clustering</span></a>, <a href="https://sigmoid.social/tags/classification" class="mention hashtag" rel="tag">#<span>classification</span></a>, and <a href="https://sigmoid.social/tags/InformationRetrieval" class="mention hashtag" rel="tag">#<span>InformationRetrieval</span></a>. In this tutorial, you can find a discussion of five commonly used metrics: EMD, <a href="https://sigmoid.social/tags/KullbackLeiblerDivergence" class="mention hashtag" rel="tag">#<span>KullbackLeiblerDivergence</span></a> (KL Divergence), <a href="https://sigmoid.social/tags/JensenShannonDivergence" class="mention hashtag" rel="tag">#<span>JensenShannonDivergence</span></a> (JS Divergence), <a href="https://sigmoid.social/tags/TotalVariationDistance" class="mention hashtag" rel="tag">#<span>TotalVariationDistance</span></a> (TV Distance), and <a href="https://sigmoid.social/tags/BhattacharyyaDistance" class="mention hashtag" rel="tag">#<span>BhattacharyyaDistance</span></a>. </p><p>🌎 <a href="https://www.fabriziomusacchio.com/blog/2023-07-28-probability_density_metrics/" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">3-07-28-probability_density_metrics/</span></a></p>
Fabrizio Musacchio<p>The <a href="https://sigmoid.social/tags/Wasserstein" class="mention hashtag" rel="tag">#<span>Wasserstein</span></a> distance (<a href="https://sigmoid.social/tags/EMD" class="mention hashtag" rel="tag">#<span>EMD</span></a>), sliced Wasserstein distance (<a href="https://sigmoid.social/tags/SWD" class="mention hashtag" rel="tag">#<span>SWD</span></a>), and the <a href="https://sigmoid.social/tags/L2norm" class="mention hashtag" rel="tag">#<span>L2norm</span></a> are common <a href="https://sigmoid.social/tags/metrics" class="mention hashtag" rel="tag">#<span>metrics</span></a> used to quantify the ‘distance’ between two distributions. This tutorial compares these three metrics and discusses their advantages and disadvantages.</p><p>🌎 <a href="https://www.fabriziomusacchio.com/blog/2023-07-26-wasserstein_vs_l2_norm/" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">3-07-26-wasserstein_vs_l2_norm/</span></a></p><p><a href="https://sigmoid.social/tags/OptimalTransport" class="mention hashtag" rel="tag">#<span>OptimalTransport</span></a> <a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="tag">#<span>MachineLearning</span></a></p>
Fabrizio Musacchio<p>This tutorial takes a different approach to explain the <a href="https://sigmoid.social/tags/Wasserstein" class="mention hashtag" rel="tag">#<span>Wasserstein</span></a> distance (<a href="https://sigmoid.social/tags/EMD" class="mention hashtag" rel="tag">#<span>EMD</span></a>) by approximating the <a href="https://sigmoid.social/tags/EMD" class="mention hashtag" rel="tag">#<span>EMD</span></a> with cumulative distribution functions (<a href="https://sigmoid.social/tags/CDF" class="mention hashtag" rel="tag">#<span>CDF</span></a>), providing a more intuitive understanding of the metric. </p><p>🌎 <a href="https://www.fabriziomusacchio.com/blog/2023-07-24-wasserstein_distance_cdf_approximation/" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">3-07-24-wasserstein_distance_cdf_approximation/</span></a></p><p><a href="https://sigmoid.social/tags/OptimalTransport" class="mention hashtag" rel="tag">#<span>OptimalTransport</span></a></p>
Fabrizio Musacchio<p>Calculating the <a href="https://sigmoid.social/tags/Wasserstein" class="mention hashtag" rel="tag">#<span>Wasserstein</span></a> distance (<a href="https://sigmoid.social/tags/EMD" class="mention hashtag" rel="tag">#<span>EMD</span></a>) 📈 can be computational costly when using <a href="https://sigmoid.social/tags/LinearProgramming" class="mention hashtag" rel="tag">#<span>LinearProgramming</span></a>. The <a href="https://sigmoid.social/tags/Sinkhorn" class="mention hashtag" rel="tag">#<span>Sinkhorn</span></a> algorithm provides a computationally efficient method for approximating the EMD, making it a practical choice for many applications, especially for large datasets 💫. Here is another tutorial, showing how to solve <a href="https://sigmoid.social/tags/OptimalTransport" class="mention hashtag" rel="tag">#<span>OptimalTransport</span></a> problem using the Sinkhorn algorithm in <a href="https://sigmoid.social/tags/Python" class="mention hashtag" rel="tag">#<span>Python</span></a> 🐍</p><p>🌎 <a href="https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance_sinkhorn/" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">3-07-23-wasserstein_distance_sinkhorn/</span></a></p>
Fabrizio Musacchio<p>The <a href="https://sigmoid.social/tags/Wasserstein" class="mention hashtag" rel="tag">#<span>Wasserstein</span></a> distance 📐, aka Earth Mover’s Distance (<a href="https://sigmoid.social/tags/EMD" class="mention hashtag" rel="tag">#<span>EMD</span></a>), provides a robust and insightful approach for comparing <a href="https://sigmoid.social/tags/ProbabilityDistributions" class="mention hashtag" rel="tag">#<span>ProbabilityDistributions</span></a> 📊. I’ve composed a <a href="https://sigmoid.social/tags/Python" class="mention hashtag" rel="tag">#<span>Python</span></a> tutorial 🐍 that explains the <a href="https://sigmoid.social/tags/OptimalTransport" class="mention hashtag" rel="tag">#<span>OptimalTransport</span></a> problem required to calculate EMD. It also shows how to solve the OT problem and calculate the EMD using the Python Optimal Transport (POT) library. Feel free to use and share it 🤗 </p><p>🌎 <a href="https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance/" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">3-07-23-wasserstein_distance/</span></a></p>