{"id":2255,"date":"2023-03-23T13:19:01","date_gmt":"2023-03-23T12:19:01","guid":{"rendered":"https:\/\/www.uni.lu\/fstm-fr\/events\/adversarial-examples-bugs-features-or-just-categorical-learning-in-a-small-world\/"},"modified":"2023-03-23T13:19:01","modified_gmt":"2023-03-23T12:19:01","slug":"adversarial-examples-bugs-features-or-just-categorical-learning-in-a-small-world","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/fstm-fr\/events\/adversarial-examples-bugs-features-or-just-categorical-learning-in-a-small-world\/","title":{"rendered":"Adversarial Examples: bugs, features, or just categorical learning in a small world?"},"content":{"rendered":"<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\"><p><strong>Abstract:<\/strong><\/p><p>When adversarial examples were introduced for the first time in 2014, they ruined some of the most ambitious dreams for the future of deep learning and AI in general. The earliest reaction was, of course, to develop defense methods by exploring mathematical possibilities of robust learning against specific attacks. However, simultaneously, there have been efforts contributed to explaining this deep learning\u2019s vulnerability on higher semantic levels.<\/p><p>This presentation consists of an overview of some of the most popular adversarial attacks, followed by explaining the math behind the earliest developed gradient-based attacks. After that, we will review a couple of studies that give a different interpretation of adversarial examples, not as a\u00a0 weakness, but as an outcome of a different learning algorithm, compared to that of humans. Finally I will give a summary of my curious work with a basic adversarial attack to understand and explain, so to say, \u2018cognition\u2019 in neural networks.<\/p>\n<h3 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"speaker\"\n    >\nSpeaker:<\/h3>\n<p>Sahar Niknam is a doctoral researcher in the Department of Computer Science, in the Faculty of Science Technology and Medicine of the University of Luxembourg.<\/p>\n<h3 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"machine-learning-seminar\"\n    >\nMachine Learning Seminar<\/h3>\n<p>The Machine Learning Seminar is a regular weekly seminar series aiming to harbour presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. The focus is thus on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences. More information about the ML Seminar, together with video recordings from past meetings you will find here: <a href=\"https:\/\/legato-team.eu\/seminars\/\" target=\"_self\" title=\"\" rel=\"noopener\">https:\/\/legato-team.eu\/seminars\/<\/a><\/p><p>Contact:\u00a0Dr. Jakub Lengiewicz\u00a0<\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"","protected":false},"author":0,"featured_media":2256,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"event_start_date":"2023-03-29 10:00:00","event_end_date":"2023-03-29 11:00:00","event_speaker_name":"Sahar Niknam (Department of Computer Science, Faculty of Science Technology and Medicine, University of Luxembourg)","event_speaker_link":"","event_is_online":false,"event_location":"Virtual","event_street":"","event_location_link":"","event_zip_code":"","event_city":"","event_country":"LU"},"events-topic":[302],"events-type":[],"organisation":[42,24],"authorship":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Adversarial Examples: bugs, features, or just categorical learning in a small world? - FSTM I Uni.lu<\/title>\n<meta name=\"description\" content=\"Abstract:When adversarial examples were introduced for the first time in 2014, they ruined some of the most ambitious dreams for the future of deep\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.uni.lu\/fstm-fr\/events\/adversarial-examples-bugs-features-or-just-categorical-learning-in-a-small-world\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Adversarial Examples: bugs, features, or just categorical learning in a small world?\" \/>\n<meta property=\"og:description\" content=\"Abstract:When adversarial examples were introduced for the first time in 2014, they ruined some of the most ambitious dreams for the future of deep\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.uni.lu\/fstm-fr\/events\/adversarial-examples-bugs-features-or-just-categorical-learning-in-a-small-world\/\" \/>\n<meta property=\"og:site_name\" content=\"FSTM FR\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/fstm.uni.lu\/\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2026\/03\/03111744\/FSTM_SM-Profile_1600x1600px-scaled.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"2560\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/events\/adversarial-examples-bugs-features-or-just-categorical-learning-in-a-small-world\/\",\"url\":\"https:\/\/www.uni.lu\/fstm-fr\/events\/adversarial-examples-bugs-features-or-just-categorical-learning-in-a-small-world\/\",\"name\":\"Adversarial Examples: bugs, features, or just categorical learning in a small world? 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