<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Interpretability with Accurate Small Models · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1905.01520/</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/1905.01520/feed.xml" rel="self" type="application/rss+xml" /><item><title>Interpretability with Accurate Small Models — mentioned by abhgh</title><link>https://news.ycombinator.com/item?id=22177486</link><guid isPermaLink="false">urn:hackerpapers:mention:1905.01520:22177486</guid><pubDate>Wed, 29 Jan 2020 04:36:47 GMT</pubDate><description>&lt;p&gt;I am working on something I am calling &amp;quot;compact models&amp;quot; (as part of a PhD): techniques to pack more information into Machine Learning models when their size is constrained in some way. I have put up some of our work here [1] - it has been interesting so far, and the results are promising. I would like to release a Python library soon, well, ...in a few months - my PhD is part-time, I have a full time day job and time management is a pain. [1] https://arxiv.org/abs/1905.01520&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1905.01520/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
