{"id":194601,"date":"2024-08-15T17:22:43","date_gmt":"2024-08-15T22:22:43","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2024\/08\/view-a-pdf-of-the-paper-titled-ensemble-everything-everywhere-multi-scale-aggregation-for-adversarial-robustness-by-stanislav-fort-and-1-other-authors"},"modified":"2024-08-15T17:22:43","modified_gmt":"2024-08-15T22:22:43","slug":"view-a-pdf-of-the-paper-titled-ensemble-everything-everywhere-multi-scale-aggregation-for-adversarial-robustness-by-stanislav-fort-and-1-other-authors","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2024\/08\/view-a-pdf-of-the-paper-titled-ensemble-everything-everywhere-multi-scale-aggregation-for-adversarial-robustness-by-stanislav-fort-and-1-other-authors","title":{"rendered":"View a PDF of the paper titled Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness, by Stanislav Fort and 1 other authors"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/view-a-pdf-of-the-paper-titled-ensemble-everything-everywhere-multi-scale-aggregation-for-adversarial-robustness-by-stanislav-fort-and-1-other-authors2.jpg\"><\/a><\/p>\n<p>Inspired by biology we 1) get adversarial robustness + interpretability for free, 2) turn classifiers into generators &amp; 3) design attacks on vLLMs.<\/p>\n<p>Stanislav Fort, Balaji Lakshminarayanan August 2024 <a href=\"https:\/\/www.arxiv.org\/abs\/2408\">https:\/\/www.arxiv.org\/abs\/2408<\/a>.<\/p>\n<hr>\n<p>Adversarial examples pose a significant challenge to the robustness, reliability and alignment of deep neural networks. We propose a novel, easy-to-use approach to achieving high-quality representations that lead to adversarial robustness through the use of multi-resolution input representations and dynamic self-ensembling of intermediate layer predictions. We demonstrate that intermediate layer predictions exhibit inherent robustness to adversarial attacks crafted to fool the full classifier, and propose a robust aggregation mechanism based on Vickrey auction that we call \\textit{CrossMax} to dynamically ensemble them.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Inspired by biology we 1) get adversarial robustness + interpretability for free, 2) turn classifiers into generators &amp; 3) design attacks on vLLMs. Stanislav Fort, Balaji Lakshminarayanan August 2024 https:\/\/www.arxiv.org\/abs\/2408. Adversarial examples pose a significant challenge to the robustness, reliability and alignment of deep neural networks. We propose a novel, easy-to-use approach to achieving high-quality [\u2026]<\/p>\n","protected":false},"author":709,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-194601","post","type-post","status-publish","format-standard","hentry","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/194601","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/709"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=194601"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/194601\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=194601"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=194601"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=194601"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}