<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Training Very Deep Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1507.06228/</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/1507.06228/feed.xml" rel="self" type="application/rss+xml" /><item><title>Training Very Deep Networks — mentioned by jisaacso</title><link>https://news.ycombinator.com/item?id=12165847</link><guid isPermaLink="false">urn:hackerpapers:mention:1507.06228:12165847</guid><pubDate>Tue, 26 Jul 2016 14:30:51 GMT</pubDate><description>&lt;p&gt;Hey thanks for the great questions! (1) The NN uses two convolutional units and a fully connected softmax layer. Relative to Inception V3 or highway networks, this is _not_ a very deep architecture. I was looking for a balance between accuracy and training time (trained on my MBP). (2) I looked into other neural architectures (LSTM and several fully connected) without much difference in performance. If you take a look at the physionet google group, there are a number of other methods evaluated (logistic regression, SVMs, etc.) (3) I did vary hyperparameters and saw a dropout of ~0.45 and…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1507.06228/"&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>
