<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Learning | Trustworthy AI Systems Lab (TAS Lab)</title><link>https://tas-lab.org/tag/machine-learning/</link><atom:link href="https://tas-lab.org/tag/machine-learning/index.xml" rel="self" type="application/rss+xml"/><description>Machine Learning</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://tas-lab.org/media/icon_hu_9cf092d4d003c2c7.png</url><title>Machine Learning</title><link>https://tas-lab.org/tag/machine-learning/</link></image><item><title>Balancing Realism and Attack Efficacy: Adversarial Texture Generation From Authentic Clothing Patterns</title><link>https://tas-lab.org/publication/2025-balancing-realism-and-attack-efficacy-adversarial-texture-generation-from-authen/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://tas-lab.org/publication/2025-balancing-realism-and-attack-efficacy-adversarial-texture-generation-from-authen/</guid><description/></item><item><title>LFPD: Local-Feature-Powered Defense Against Adaptive Backdoor Attacks</title><link>https://tas-lab.org/publication/2024-lfpd-local-feature-powered-defense-against-adaptive-backdoor-attacks/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://tas-lab.org/publication/2024-lfpd-local-feature-powered-defense-against-adaptive-backdoor-attacks/</guid><description/></item><item><title>Unsupervised contaminated user profile identification against shilling attack in recommender system</title><link>https://tas-lab.org/publication/2024-unsupervised-contaminated-user-profile-identification-against-shilling-attack-in/</link><pubDate>Mon, 01 Jan 2024 00:00:00 +0000</pubDate><guid>https://tas-lab.org/publication/2024-unsupervised-contaminated-user-profile-identification-against-shilling-attack-in/</guid><description/></item><item><title>Distribution-based Adversarial Filter Feature Selection against Evasion Attack</title><link>https://tas-lab.org/publication/2021-distribution-based-adversarial-filter-feature-selection/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://tas-lab.org/publication/2021-distribution-based-adversarial-filter-feature-selection/</guid><description/></item><item><title>Dual-Path Model for Person Re-Identification Under Cloth Changing</title><link>https://tas-lab.org/publication/2020-dual-path-model-for-person-re-identification-under-cloth-changing/</link><pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate><guid>https://tas-lab.org/publication/2020-dual-path-model-for-person-re-identification-under-cloth-changing/</guid><description/></item><item><title>Segmentation Based Backdoor Attack Detection</title><link>https://tas-lab.org/publication/2020-segmentation-based-backdoor-attack-detection/</link><pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate><guid>https://tas-lab.org/publication/2020-segmentation-based-backdoor-attack-detection/</guid><description/></item><item><title>Class Size Variance Minimization to Metric Learning for Dish Identification</title><link>https://tas-lab.org/publication/2019-class-size-variance-minimization-to-metric-learning-for-dish-identification/</link><pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate><guid>https://tas-lab.org/publication/2019-class-size-variance-minimization-to-metric-learning-for-dish-identification/</guid><description/></item><item><title>Experimental study on stacked autoencoder on insufficient training samples</title><link>https://tas-lab.org/publication/2017-experimental-study-on-stacked-autoencoder-on-insufficient-training-samples/</link><pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate><guid>https://tas-lab.org/publication/2017-experimental-study-on-stacked-autoencoder-on-insufficient-training-samples/</guid><description/></item></channel></rss>