<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Neural ODEs on Cecília Coelho</title><link>https://ceciliacoelho.github.io/tags/neural-odes/</link><description>Recent content in Neural ODEs on Cecília Coelho</description><generator>Hugo -- 0.128.0</generator><language>en</language><lastBuildDate>Wed, 12 Feb 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://ceciliacoelho.github.io/tags/neural-odes/index.xml" rel="self" type="application/rss+xml"/><item><title>Neural Chronos ODE: Modelling bidirectional temporal patterns in time-series data</title><link>https://ceciliacoelho.github.io/papers/paper1/</link><pubDate>Wed, 12 Feb 2025 00:00:00 +0000</pubDate><guid>https://ceciliacoelho.github.io/papers/paper1/</guid><description>This work introduces Neural Chronos Ordinary Differential Equations (Neural CODE), a deep neural network architecture that fits a continuous-time ODE dynamics for predicting the chronology of a system both forward and backward in time. Published in Expert Systems with Applications, 2025.</description></item></channel></rss>