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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A guide to convolution arithmetic for deep learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1603.07285/</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/1603.07285/feed.xml" rel="self" type="application/rss+xml" /><item><title>A guide to convolution arithmetic for deep learning — HN submission</title><link>https://news.ycombinator.com/item?id=32438899</link><guid isPermaLink="false">urn:hackerpapers:submission:1603.07285:32438899</guid><pubDate>Fri, 12 Aug 2022 14:09:17 GMT</pubDate><description>&lt;p&gt;A guide to convolution arithmetic for deep learning&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;2 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1603.07285/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>A guide to convolution arithmetic for deep learning — HN submission</title><link>https://news.ycombinator.com/item?id=23991629</link><guid isPermaLink="false">urn:hackerpapers:submission:1603.07285:23991629</guid><pubDate>Wed, 29 Jul 2020 21:10:16 GMT</pubDate><description>&lt;p&gt;A guide to convolution arithmetic for deeplearning&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1603.07285/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>A guide to convolution arithmetic for deep learning — HN submission</title><link>https://news.ycombinator.com/item?id=22407419</link><guid isPermaLink="false">urn:hackerpapers:submission:1603.07285:22407419</guid><pubDate>Mon, 24 Feb 2020 19:23:57 GMT</pubDate><description>&lt;p&gt;A guide to convolution arithmetic for deep learning [pdf]&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;4 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1603.07285/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>A guide to convolution arithmetic for deep learning — HN submission</title><link>https://news.ycombinator.com/item?id=16227615</link><guid isPermaLink="false">urn:hackerpapers:submission:1603.07285:16227615</guid><pubDate>Thu, 25 Jan 2018 00:15:27 GMT</pubDate><description>&lt;p&gt;A guide to convolution arithmetic for deep learning&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;2 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1603.07285/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>A guide to convolution arithmetic for deep learning — HN submission</title><link>https://news.ycombinator.com/item?id=14450726</link><guid isPermaLink="false">urn:hackerpapers:submission:1603.07285:14450726</guid><pubDate>Wed, 31 May 2017 06:23:54 GMT</pubDate><description>&lt;p&gt;A guide to convolution arithmetic for deep learning&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;3 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1603.07285/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>A guide to convolution arithmetic for deep learning — mentioned by transcranial</title><link>https://news.ycombinator.com/item?id=12620312</link><guid isPermaLink="false">urn:hackerpapers:mention:1603.07285:12620312</guid><pubDate>Sat, 01 Oct 2016 23:12:06 GMT</pubDate><description>&lt;p&gt;And https://arxiv.org/abs/1603.07285 for the corresponding paper. Really clear and easy-to-understand explanation of some of the math.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1603.07285/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>A guide to convolution arithmetic for deep learning — mentioned by rasbt</title><link>https://news.ycombinator.com/item?id=11843931</link><guid isPermaLink="false">urn:hackerpapers:mention:1603.07285:11843931</guid><pubDate>Mon, 06 Jun 2016 00:33:30 GMT</pubDate><description>&lt;p&gt;I agree with you, it could surely expand this answer (I am the person who wrote this little answer). Since this was originally just an answer to a question I answered via email (if I remember correctly), I didn&amp;#x27;t go into too much depth regarding ConvNets, because I just wanted to answer this question &amp;quot;briefly&amp;quot; in this mail. But if there&amp;#x27;s a demand for that (if it&amp;#x27;s useful to others) I may end up writing a tutorial on ConvNets one day :). However, there&amp;#x27;s already an excellent one out there, I highly recommend Dumoulin &amp;amp; Visin&amp;#x27;s &amp;quot;A guide to convolution arithmetic for deep learning&amp;quot; at…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1603.07285/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item><item><title>A guide to convolution arithmetic for deep learning — HN submission</title><link>https://news.ycombinator.com/item?id=11355384</link><guid isPermaLink="false">urn:hackerpapers:submission:1603.07285:11355384</guid><pubDate>Thu, 24 Mar 2016 18:57:36 GMT</pubDate><description>&lt;p&gt;A guide to convolution arithmetic for deep learning&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;6 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1603.07285/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
