{"id":7796,"date":"2022-12-19T17:27:28","date_gmt":"2022-12-19T14:27:28","guid":{"rendered":"https:\/\/msc.dit.hua.gr\/?p=7796"},"modified":"2022-12-19T17:27:29","modified_gmt":"2022-12-19T14:27:29","slug":"the-machine-learning-of-time-and-dynamics-for-vision-sciences-engineering","status":"publish","type":"post","link":"https:\/\/mscdev.dit.hua.gr\/en\/the-machine-learning-of-time-and-dynamics-for-vision-sciences-engineering\/","title":{"rendered":"The Machine Learning of Time and Dynamics for Vision, Sciences, Engineering"},"content":{"rendered":"<p><strong><em>Dr. Efstratios Gavves <\/em><\/strong>is an Associate Professor at the University of Amsterdam teaching Deep Learning at the MSc in AI, an ELLIS Scholar, and co-founder of Ellogon.AI. He is a director of the QUVA Deep Vision Lab with Qualcomm, and the POP-AART Lab with the Netherlands Cancer Institute and Elekta. Efstratios received the ERC Career Starting Grant 2020 and NWO VIDI grant 2020 to research on the Computational Learning of Time for spatiotemporal sequences and video. His background is in computer vision, and now focusing on temporal machine learning and dynamical systems, efficient computer vision, and machine learning for oncology.\u00a0He is currently supervising 15 PhD students on various topics pertaining theory and applications of Deep Learning and Computer Vision, including neural network, dynamical dystems, and physical laws in learning algorithms, causal and object-centric representation learning, open-world video understanding, deep probabilistic and generative models, deep learning geometry, and applications to histopathological analysis and to adaptive radiotherapy.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><strong><em>\u0391\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03b5\u03af&nbsp;\u03c3\u03cd\u03bd\u03c4\u03bf\u03bc\u03b7&nbsp;\u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03b1\u03c6\u03ae<\/em><\/strong><\/p>\n\n\n\n<p><em>Visual artificial intelligence automatically interprets what happens in visual data like videos. Today\u2019s research strives with queries like: \u201cIs this person playing basketball?\u201d; \u201cFind the location of the brain stroke\u201d; or \u201cTrack the glacier fractures in satellite footage\u201d. All these queries are about visual observations already taken place. Today\u2019s algorithms focus on explaining past visual observations. Naturally, not all queries are about the past: \u201cWill this person draw something in or out of their pocket?\u201d; \u201cWhere will the tumour be in 5 seconds given breathing patterns and moving organs?\u201d; or, \u201cHow will the glacier fracture given the current motion and melting patterns?\u201d. For these queries and all others, the next generation of visual algorithms must expect what happens next given past visual observations. Visual artificial intelligence must also be able to prevent before the fact, rather than explain only after it. In this talk, I will present my vision on what these algorithms should look like, and investigate possible synergies with other fields of science, like biomedical research, astronomy and others. Furthermore, I will present some recent works and applications in this direction.<\/em><\/p>\n\n\n\n<p><em>\u0397 \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae \u03bd\u03bf\u03b7\u03bc\u03bf\u03c3\u03cd\u03bd\u03b7 \u03c0\u03bf\u03c5 \u03c3\u03c7\u03b5\u03c4\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c4\u03b7\u03bd \u03b1\u03bd\u03c4\u03af\u03bb\u03b7\u03c8\u03b7 \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b5\u03be\u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03bf\u03c0\u03c4\u03b9\u03ba\u03ce\u03bd \u03b5\u03c1\u03b5\u03b8\u03b9\u03c3\u03bc\u03ac\u03c4\u03c9\u03bd \u03b5\u03c3\u03c4\u03b9\u03ac\u03b6\u03b5\u03b9 \u03c3\u03c4\u03bf \u03bd\u03b1 \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03cd\u03b5\u03b9 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03cc\u03c0\u03c9\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03ae \u03b2\u03b9\u03bd\u03c4\u03b5\u03bf \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ac\u03b5\u03b9 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\u03b5\u03c0\u03b9\u03c3\u03c4\u03b7\u03bc\u03bf\u03bd\u03b9\u03ba\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, \u03ba\u03b1\u03b8\u03ce\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03c0\u03c1\u03cc\u03c3\u03c6\u03b1\u03c4\u03b7 \u03bc\u03bf\u03c5 \u03ad\u03c1\u03b5\u03c5\u03bd\u03b1 \u03c0\u03c1\u03bf\u03c2 \u03c4\u03b7\u03bd \u03ba\u03b1\u03c4\u03b5\u03cd\u03b8\u03c5\u03bd\u03c3\u03b7 \u03b1\u03c5\u03c4\u03ae.&nbsp;&nbsp;<\/em><\/p>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>22\/12\/2022 | \u039f\u03bc\u03b9\u03bb\u03b7\u03c4\u03ae\u03c2: Dr. Efstratios Gavves, Associate Professor, University of Amsterdam<\/p>","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[179,193],"tags":[],"class_list":["post-7796","post","type-post","status-publish","format-standard","hentry","category-anakoinoseis","category-seminaria"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.5) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>The Machine Learning of Time and Dynamics for Vision, Sciences, Engineering - MSc | Informatics and Telematics | Harokopio University<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscdev.dit.hua.gr\/en\/the-machine-learning-of-time-and-dynamics-for-vision-sciences-engineering\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" 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