<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Manuscript | MODE Collaboration</title><link>https://mode-demo.github.io/publication_types/manuscript/</link><atom:link href="https://mode-demo.github.io/publication_types/manuscript/index.xml" rel="self" type="application/rss+xml"/><description>Manuscript</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Wed, 01 Jan 2025 00:00:00 +0000</lastBuildDate><image><url>https://mode-demo.github.io/media/icon_hu_ebbff252c19052d0.png</url><title>Manuscript</title><link>https://mode-demo.github.io/publication_types/manuscript/</link></image><item><title>End-to-End Detector Optimization with Diffusion models: A Case Study in Sampling Calorimeters</title><link>https://mode-demo.github.io/publication/schmidt-2025-endtoenddetectoroptimizationdiffusion/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://mode-demo.github.io/publication/schmidt-2025-endtoenddetectoroptimizationdiffusion/</guid><description/></item><item><title>Deep Regression of Muon Energy with a K-Nearest Neighbor Algorithm</title><link>https://mode-demo.github.io/publication/dorigo-2022-deepregressionmuonenergy/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://mode-demo.github.io/publication/dorigo-2022-deepregressionmuonenergy/</guid><description/></item><item><title>Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper</title><link>https://mode-demo.github.io/publication/dorigo-2022-endtoendoptimizationparticlephysics/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://mode-demo.github.io/publication/dorigo-2022-endtoendoptimizationparticlephysics/</guid><description/></item><item><title>Adversarial Variational Optimization of Non-Differentiable Simulators</title><link>https://mode-demo.github.io/publication/louppe-2020-adversarialvariationaloptimizationnondifferentiable/</link><pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate><guid>https://mode-demo.github.io/publication/louppe-2020-adversarialvariationaloptimizationnondifferentiable/</guid><description/></item><item><title>Approximating Likelihood Ratios with Calibrated Discriminative Classifiers</title><link>https://mode-demo.github.io/publication/cranmer-2016-approximatinglikelihoodratioscalibrated/</link><pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate><guid>https://mode-demo.github.io/publication/cranmer-2016-approximatinglikelihoodratioscalibrated/</guid><description/></item></channel></rss>