<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Visualization on Abhiraj Bibhar</title><link>https://abhiraj.pages.dev/tags/visualization/</link><description>Recent content in Visualization on Abhiraj Bibhar</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sun, 01 Oct 2023 00:00:00 +0000</lastBuildDate><atom:link href="https://abhiraj.pages.dev/tags/visualization/index.xml" rel="self" type="application/rss+xml"/><item><title>Interactive Visualization Techniques for Deep Model Debugging</title><link>https://abhiraj.pages.dev/publications/interactive-visualization-deep-model-debugging/</link><pubDate>Sun, 01 Oct 2023 00:00:00 +0000</pubDate><guid>https://abhiraj.pages.dev/publications/interactive-visualization-deep-model-debugging/</guid><description>&lt;h2 id="abstract"&gt;Abstract&lt;/h2&gt;&#10;&lt;p&gt;Debugging deep learning models remains largely a manual, intuition-driven process. We present GlassBox, an interactive visualization system that lets researchers inspect layer activations, attention weights, and gradient flow simultaneously across a model&amp;rsquo;s forward and backward passes, with linked views that highlight where behavior diverges from expectations.&lt;/p&gt;</description></item></channel></rss>