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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1901.10055/</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/1901.10055/feed.xml" rel="self" type="application/rss+xml" /><item><title>Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition — mentioned by JulianSlzr</title><link>https://news.ycombinator.com/item?id=19375265</link><guid isPermaLink="false">urn:hackerpapers:mention:1901.10055:19375265</guid><pubDate>Wed, 13 Mar 2019 02:53:42 GMT</pubDate><description>&lt;p&gt;We explored self-attention + CTC in our ICASSP 2019 paper (https://arxiv.org/abs/1901.10055). Our implementation uses internal infra, so we&amp;#x27;re a ways from releasing code :( Hoping paper details suffice and help with the parameter search, and happy to respond to questions over e-mail. Would love to see an open-source implementation with local or directed attention built out!&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1901.10055/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>eess.AS</category></item></channel></rss>
