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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>From data to functa: Your data point is a function and you can treat it like one · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2201.12204/</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/2201.12204/feed.xml" rel="self" type="application/rss+xml" /><item><title>From data to functa: Your data point is a function and you can treat it like one — mentioned by stefanpie</title><link>https://news.ycombinator.com/item?id=37384902</link><guid isPermaLink="false">urn:hackerpapers:mention:2201.12204:37384902</guid><pubDate>Mon, 04 Sep 2023 20:47:39 GMT</pubDate><description>&lt;p&gt;Another approach to neural-based data representation / encoding / decoding is Implicit Neural Representations (INRs). For INRs, you overfit a neural network, typically a Multilayer Perceptron (MLP)[1], to a single data point. For instance, in the case of a single image (the data point), the inputs would be the xy pixel coordinates—usually scaled from [0, n_pixels]^2 to either [0, 1]^2 or [-1, 1]^2—and the outputs would be the RGB values of each pixel. Once trained on pixels from this singular image, the INR model essentially becomes a representation of that data point. The image can then be…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2201.12204/"&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>
