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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Sebastian Nowozin</author_name><author_url>https://www.microsoft.com/en-us/research/people/senowozi/</author_url><title>Variational Continual Learning - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="UqQAfafYzb"&gt;&lt;a href="https://www.microsoft.com/en-us/research/video/variational-continual-learning/"&gt;Variational Continual Learning&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/video/variational-continual-learning/embed/#?secret=UqQAfafYzb" width="600" height="338" title="&#x201C;Variational Continual Learning&#x201D; &#x2014; Microsoft Research" data-secret="UqQAfafYzb" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><thumbnail_url>https://www.microsoft.com/en-us/research/wp-content/uploads/2018/03/40822.jpg</thumbnail_url><thumbnail_width>1278</thumbnail_width><thumbnail_height>718</thumbnail_height><description>This talk introduces variational continual learning, a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks. The framework can successfully train both deep discriminative models and deep generative models in complex continual learning settings where existing tasks evolve over time and [&hellip;]</description></oembed>
