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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>One-Shot Learning in Discriminative Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1707.05562/</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/1707.05562/feed.xml" rel="self" type="application/rss+xml" /><item><title>One-Shot Learning in Discriminative Neural Networks — HN submission</title><link>https://news.ycombinator.com/item?id=23987353</link><guid isPermaLink="false">urn:hackerpapers:submission:1707.05562:23987353</guid><pubDate>Wed, 29 Jul 2020 14:57:53 GMT</pubDate><description>&lt;p&gt;Launch HN: Humanloop (YC S20) – A platform to annotate, train and deploy NLP&lt;/p&gt;&lt;p&gt;Hey HN. We’re Peter, Raza and Jordan of Humanloop (https://humanloop.com) and we’re building a low code platform to annotate data, rapidly train and then deploy Natural Language Processing (NLP) models. We use active learning research to make this possible with 5-10x less labelled data. We’ve worked on large machine learning products in industry (Alexa, text-to-speech systems at Google and in insurance modelling) and seen first-hand the huge efforts required to get these systems trained, deployed and working well in production. Despite huge progress in pretrained models (BERT, GPT-3), one of…&lt;/p&gt;&lt;p&gt;157 points; 33 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1707.05562/"&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>
