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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Pipeline Embeddings for AutoML · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2305.14009/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2305.14009/feed.xml" rel="self" type="application/rss+xml" /><item><title>Deep Pipeline Embeddings for AutoML — mentioned by mlminer</title><link>https://news.ycombinator.com/item?id=36735735</link><guid isPermaLink="false">urn:hackerpapers:mention:2305.14009:36735735</guid><pubDate>Sat, 15 Jul 2023 11:53:00 GMT</pubDate><description>&lt;p&gt;A powerful approach for AutoML. It can be applied to any type of data, e.g. language, images or tables! Code: https://github.com/releaunifreiburg/DeepPipe Blog Post: https://releaunifreiburg.github.io/deepppipe/ Paper: https://arxiv.org/abs/2305.14009&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2305.14009/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Deep Pipeline Embeddings for AutoML — HN submission</title><link>https://news.ycombinator.com/item?id=36211556</link><guid isPermaLink="false">urn:hackerpapers:submission:2305.14009:36211556</guid><pubDate>Tue, 06 Jun 2023 12:23:57 GMT</pubDate><description>&lt;p&gt;State-of-the-Art Deep Learning Pipelines Optimization via Vector Embeddings&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;6 points; 1 discussion reply on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2305.14009/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
