{"id":2172,"date":"2022-11-28T10:33:54","date_gmt":"2022-11-28T09:33:54","guid":{"rendered":"https:\/\/www.uni.lu\/fstm-fr\/events\/machine-learning-seminar-meeting-concept-based-explanations-for-convolutional-neural-networks\/"},"modified":"2022-11-28T10:33:54","modified_gmt":"2022-11-28T09:33:54","slug":"machine-learning-seminar-meeting-concept-based-explanations-for-convolutional-neural-networks","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/fstm-fr\/events\/machine-learning-seminar-meeting-concept-based-explanations-for-convolutional-neural-networks\/","title":{"rendered":"Machine Learning Seminar meeting: Concept-based explanations for convolutional neural networks"},"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>Convolutional neural networks (CNNs) are increasingly being used in critical systems, where robustness and alignment are crucial. In this context, the field of explainable artificial intelligence has proposed the generation of high-level explanations of the prediction process of CNNs through concept extraction. While these methods can detect whether or not a concept is present in an image, they are unable to determine its location. What is more, a fair comparison of such approaches is difficult due to a lack of proper validation procedures. In this talk, we discuss a novel method for automatic concept extraction and localization based on representations obtained through pixel-wise aggregations of CNN activation maps. Further, we introduce a process for the validation of concept-extraction techniques based on synthetic datasets with pixel-wise annotations of their main components, reducing the need for human intervention.<\/p><p><strong><a href=\"https:\/\/www.dsme.rwth-aachen.de\/cms\/DSME\/Das-Institut\/Team\/~ocdhq\/Andres-Posada-Moreno\/?lidx=1\" target=\"_blank\" title=\"\" rel=\"noopener\">Andres Posada Moreno<\/a><\/strong> is a PhD Student, Institute for Data Science in Mechanical Engineering at the RWTH Aachen University. His expertise lies in research and applied technologies in multidisciplinary environments, with a focus on artificial intelligence. He is\u00a0 experienced in the research, development, and management of AI projects in agriculture, manufacturing quality control, robotics, and railway systems. His current research focuses in the field of explainable artificial intelligence.<\/p><p>The <strong>Machine Learning Seminar<\/strong> is a regular weekly seminar series aiming to harbor 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><strong>To register please send a mail to\u00a0Dr. Jakub Lengiewicz.<\/strong><\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"<p>Abstract:Convolutional neural networks (CNNs) are increasingly being used in critical systems, where robustness and alignment are crucial. In this context, the field of explainable artificial intelligence has proposed the generation of high-level explanations of the prediction process of CNNs through concept extraction. While these methods can detect whether or not a concept is present in an image, they are unable to determine its location. What is more, a fair comparison of such approaches is difficult due to a lack of proper validation procedures. In this talk, we discuss a novel method for automatic concept extraction and localization based on representations obtained through pixel-wise aggregations of CNN activation maps. Further, we introduce a process for the validation of concept-extraction techniques based on synthetic datasets with pixel-wise annotations of their main components, reducing the need for human intervention.<\/p>\n","protected":false},"author":0,"featured_media":2173,"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":"2022-11-30 10:00:00","event_end_date":"2022-11-30 11:00:00","event_speaker_name":"Andres Posada Moreno (Institute for Data Science in Mechanical Engineering at the RWTH Aachen University)","event_speaker_link":"","event_is_online":false,"event_location":"Online","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>Machine Learning Seminar meeting: Concept-based explanations for convolutional neural networks - FSTM I Uni.lu<\/title>\n<meta name=\"description\" content=\"Abstract:Convolutional neural networks (CNNs) are increasingly being used in critical systems, where robustness and alignment are crucial. In this context, the field of explainable artificial intelligence has proposed the generation of high-level explanations of the prediction process of CNNs through concept extraction. While these methods can detect whether or not a concept is present in an image, they are unable to determine its location. What is more, a fair comparison of such approaches is difficult due to a lack of proper validation procedures. In this talk, we discuss a novel method for automatic concept extraction and localization based on representations obtained through pixel-wise aggregations of CNN activation maps. 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