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#mixture

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Hacker News<p>Entropy of a Mixture</p><p><a href="https://cgad.ski/blog/entropy-of-a-mixture.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">cgad.ski/blog/entropy-of-a-mix</span><span class="invisible">ture.html</span></a></p><p><a href="https://mastodon.social/tags/HackerNews" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HackerNews</span></a> <a href="https://mastodon.social/tags/Entropy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Entropy</span></a> <a href="https://mastodon.social/tags/Mixture" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Mixture</span></a> <a href="https://mastodon.social/tags/Thermodynamics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Thermodynamics</span></a> <a href="https://mastodon.social/tags/Science" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Science</span></a> <a href="https://mastodon.social/tags/Blog" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Blog</span></a> <a href="https://mastodon.social/tags/HackerNews" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HackerNews</span></a></p>
💧🌏 Greg Cocks<p>Classification Of Urban Morphology With Deep Learning - Application On Urban Vitality<br>--<br><a href="https://doi.org/10.1016/j.compenvurbsys.2021.101706" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.1016/j.compenvurbsy</span><span class="invisible">s.2021.101706</span></a> &lt;-- shared paper<br>--<br><a href="https://github.com/ualsg/Road-Network-Classification" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/ualsg/Road-Network-</span><span class="invisible">Classification</span></a> &lt;-- shared GitHub repository<br>--<br><a href="https://techhub.social/tags/GIS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GIS</span></a> <a href="https://techhub.social/tags/spatial" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>spatial</span></a> <a href="https://techhub.social/tags/mapping" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>mapping</span></a> <a href="https://techhub.social/tags/bigdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bigdata</span></a> <a href="https://techhub.social/tags/city" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>city</span></a> <a href="https://techhub.social/tags/cities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>cities</span></a> <a href="https://techhub.social/tags/spatialanalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>spatialanalysis</span></a> <a href="https://techhub.social/tags/spatiotemporal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>spatiotemporal</span></a> <a href="https://techhub.social/tags/urban" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>urban</span></a> <a href="https://techhub.social/tags/analysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>analysis</span></a> <a href="https://techhub.social/tags/model" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>model</span></a> <a href="https://techhub.social/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://techhub.social/tags/urbanmorphology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>urbanmorphology</span></a> <a href="https://techhub.social/tags/deeplearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>deeplearning</span></a> <a href="https://techhub.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://techhub.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://techhub.social/tags/classifaction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>classifaction</span></a> <a href="https://techhub.social/tags/network" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>network</span></a> <a href="https://techhub.social/tags/streets" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>streets</span></a> <a href="https://techhub.social/tags/routing" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>routing</span></a> <a href="https://techhub.social/tags/network" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>network</span></a> <a href="https://techhub.social/tags/connections" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>connections</span></a> <a href="https://techhub.social/tags/morphology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>morphology</span></a> <a href="https://techhub.social/tags/density" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>density</span></a> <a href="https://techhub.social/tags/proportion" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>proportion</span></a> <a href="https://techhub.social/tags/type" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>type</span></a> <a href="https://techhub.social/tags/mixture" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>mixture</span></a> <a href="https://techhub.social/tags/convolutionalneuralnetwork" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>convolutionalneuralnetwork</span></a> <a href="https://techhub.social/tags/CNN" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CNN</span></a> <a href="https://techhub.social/tags/regressionmodel" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regressionmodel</span></a> <a href="https://techhub.social/tags/OpenStreetMap" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OpenStreetMap</span></a> <a href="https://techhub.social/tags/groups" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>groups</span></a> <a href="https://techhub.social/tags/subgroups" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>subgroups</span></a> <a href="https://techhub.social/tags/vitality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>vitality</span></a> <a href="https://techhub.social/tags/indicators" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>indicators</span></a></p>
Gea-Suan Lin<p>NIST 的密碼原則將禁止服務商要求「混合字元」</p><p>在「NIST to forbid requirement of specific passwords character compo</p><p><a href="https://blog.gslin.org/archives/2024/09/27/12004/nist-%e7%9a%84%e5%af%86%e7%a2%bc%e5%8e%9f%e5%89%87%e5%b0%87%e7%a6%81%e6%ad%a2%e6%9c%8d%e5%8b%99%e5%95%86%e8%a6%81%e6%b1%82%e3%80%8c%e6%b7%b7%e5%90%88%e5%ad%97%e5%85%83%e3%80%8d/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">blog.gslin.org/archives/2024/0</span><span class="invisible">9/27/12004/nist-%e7%9a%84%e5%af%86%e7%a2%bc%e5%8e%9f%e5%89%87%e5%b0%87%e7%a6%81%e6%ad%a2%e6%9c%8d%e5%8b%99%e5%95%86%e8%a6%81%e6%b1%82%e3%80%8c%e6%b7%b7%e5%90%88%e5%ad%97%e5%85%83%e3%80%8d/</span></a></p><p><a href="https://abpe.org/tags/Computer" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Computer</span></a> <a href="https://abpe.org/tags/Murmuring" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Murmuring</span></a> <a href="https://abpe.org/tags/Security" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Security</span></a> <a href="https://abpe.org/tags/80063b" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>80063b</span></a> <a href="https://abpe.org/tags/guideline" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>guideline</span></a> <a href="https://abpe.org/tags/mixture" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>mixture</span></a> <a href="https://abpe.org/tags/nist" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>nist</span></a> <a href="https://abpe.org/tags/nsa" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>nsa</span></a> <a href="https://abpe.org/tags/password" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>password</span></a> <a href="https://abpe.org/tags/policy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>policy</span></a> <a href="https://abpe.org/tags/publication" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>publication</span></a> <a href="https://abpe.org/tags/security" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>security</span></a> <a href="https://abpe.org/tags/special" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>special</span></a> <a href="https://abpe.org/tags/standard" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>standard</span></a></p>
JMLR<p>&#39;Gaussian Mixture Models with Rare Events&#39;, by Xuetong Li, Jing Zhou, Hansheng Wang.</p><p><a href="http://jmlr.org/papers/v25/23-1245.html" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v25/23-1245.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/mixture" class="mention hashtag" rel="tag">#<span>mixture</span></a> <a href="https://sigmoid.social/tags/gaussian" class="mention hashtag" rel="tag">#<span>gaussian</span></a> <a href="https://sigmoid.social/tags/empirical" class="mention hashtag" rel="tag">#<span>empirical</span></a></p>
JMLR<p>&#39;Flexible Bayesian Product Mixture Models for Vector Autoregressions&#39;, by Suprateek Kundu, Joshua Lukemire.</p><p><a href="http://jmlr.org/papers/v25/22-0717.html" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v25/22-0717.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/mixture" class="mention hashtag" rel="tag">#<span>mixture</span></a> <a href="https://sigmoid.social/tags/multivariate" class="mention hashtag" rel="tag">#<span>multivariate</span></a> <a href="https://sigmoid.social/tags/mixtures" class="mention hashtag" rel="tag">#<span>mixtures</span></a></p>
Published papers at TMLR<p>A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models</p><p>Oleg Arenz, Philipp Dahlinger, Zihan Ye, Michael Volpp, Gerhard Neumann</p><p>Action editor: George Papamakarios.</p><p><a href="https://openreview.net/forum?id=tLBjsX4tjs" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://</span><span class="ellipsis">openreview.net/forum?id=tLBjsX</span><span class="invisible">4tjs</span></a></p><p><a href="https://sigmoid.social/tags/variational" class="mention hashtag" rel="tag">#<span>variational</span></a> <a href="https://sigmoid.social/tags/mixture" class="mention hashtag" rel="tag">#<span>mixture</span></a> <a href="https://sigmoid.social/tags/gradient" class="mention hashtag" rel="tag">#<span>gradient</span></a></p>
JMLR<p>&#39;Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process&#39;, by Cheng Zeng, Jeffrey W Miller, Leo L Duan.</p><p><a href="http://jmlr.org/papers/v24/22-0436.html" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v24/22-0436.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/clusters" class="mention hashtag" rel="tag">#<span>clusters</span></a> <a href="https://sigmoid.social/tags/clustering" class="mention hashtag" rel="tag">#<span>clustering</span></a> <a href="https://sigmoid.social/tags/mixture" class="mention hashtag" rel="tag">#<span>mixture</span></a></p>
New Submissions to TMLR<p>Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation</p><p><a href="https://openreview.net/forum?id=sbkZKBVC31" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://</span><span class="ellipsis">openreview.net/forum?id=sbkZKB</span><span class="invisible">VC31</span></a></p><p><a href="https://sigmoid.social/tags/autoencoders" class="mention hashtag" rel="tag">#<span>autoencoders</span></a> <a href="https://sigmoid.social/tags/mixdvae" class="mention hashtag" rel="tag">#<span>mixdvae</span></a> <a href="https://sigmoid.social/tags/mixture" class="mention hashtag" rel="tag">#<span>mixture</span></a></p>
JMLR<p>&#39;Regularized Joint Mixture Models&#39;, by Konstantinos Perrakis, Thomas Lartigue, Frank Dondelinger, Sach Mukherjee.</p><p><a href="http://jmlr.org/papers/v24/21-0796.html" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v24/21-0796.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/sparse" class="mention hashtag" rel="tag">#<span>sparse</span></a> <a href="https://sigmoid.social/tags/regularized" class="mention hashtag" rel="tag">#<span>regularized</span></a> <a href="https://sigmoid.social/tags/mixture" class="mention hashtag" rel="tag">#<span>mixture</span></a></p>
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