{"id":2959,"date":"2024-10-24T15:35:08","date_gmt":"2024-10-24T13:35:08","guid":{"rendered":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/?p=2959"},"modified":"2024-10-24T16:17:18","modified_gmt":"2024-10-24T14:17:18","slug":"foula-vagena-deep-learning-for-sequential-data-models-and-applications","status":"publish","type":"post","link":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/foula-vagena-deep-learning-for-sequential-data-models-and-applications\/","title":{"rendered":"Foula Vagena – Deep Learning for Sequential Data: Models and Applications"},"content":{"rendered":"

[et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”3.22″][et_pb_row _builder_version=”3.22.1″ border_color_all=”#3255c9″ border_style_all=”groove”][et_pb_column type=”1_4″ _builder_version=”3.22.1″][et_pb_text _builder_version=”3.22.1″ min_height=”11px” custom_margin=”||-25px|||” custom_padding=”||0px|||”]<\/p>\n

Foula Vagena<\/strong>
April 13, 4 PM
online (zoom)<\/span><\/p>\n

 <\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.22.1″][et_pb_text _builder_version=”3.22.1″ custom_margin=”|||||” custom_padding=”0px|||||”]<\/p>\n

Abstract<\/strong><\/h3>\n

Recurrent neural networks (RNNs) are a family of specialized neural networks for processing sequential data. They can scale to much longer sequences than would be practical for networks without sequence-based specialization and most of them can also process sequences of variable length. In this tutorial we will first describe the high level RNN architecture and outline\u00a0its most\u00a0<\/span>popular variations. We will then explain the main challenge the handling of data sequentail presents, namely long term dependenceis and summarize the different mechanisms that are employed to tackle it (i.e. gated architectures, attention mechanisms). We will go on to describe applications where RNN have been succesfule employed and we will conclude the tutorial with an illustrative RNN-supported timeseries prediction example.<\/p>\n

The Hands-On Workshop will focus on RNN supported timeseries prediction.<\/p>\n

 <\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.22.1″][et_pb_column type=”1_4″ _builder_version=”3.22.1″][et_pb_divider color=”#3255c9″ admin_label=”Divider” _builder_version=”3.22.1″][\/et_pb_divider][et_pb_text _builder_version=”3.22.1″ custom_padding=”0px|||||”]<\/p>\n

Dr Foula Vagena
(Universit\u00e9 Paris Cit\u00e9, diiP)
Zografoula Vagena is a research associate at the Data Intelligence Institute of Paris (diiP) and affiliated with the Universit\u00e9 Paris Cit\u00e9. She has been a data science researcher and practitioner for over ten years. She has worked on different analytics problems including forecasting, image processing, graph analytics, multidimensional data analysis, text processing, recommendation systems, sequential data analysis and optimization within various fields such as transportation, healthcare, retail, finance\/insurance and accounting. She has also performed research in the intersection of data management and analytics, and was a primary contributor of the MCDB\/SimSQL systems that blended data management with Bayesian statistics. She holds a PhD in data management from the University of California, Riverside.
<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.22.1″][et_pb_video_slider _builder_version=”3.22.1″ custom_margin=”10px||”][et_pb_video_slider_item src=”https:\/\/www.youtube.com\/watch?v=vzQsea76IRw” src_webm=”https:\/\/www.youtube.com\/watch?v=vzQsea76IRw” _builder_version=”3.22.1″ show_image_overlay=”off”][\/et_pb_video_slider_item][\/et_pb_video_slider][et_pb_text _builder_version=”3.22.1″ custom_margin=”60px||10px”]<\/p>\n

Click the image to see slide<\/em><\/p>\n

[\/et_pb_text][et_pb_image src=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-content\/uploads\/sites\/27\/2024\/10\/Screenshot-2024-10-24-135647.png” url=”https:\/\/view.officeapps.live.com\/op\/view.aspx?src=https%3A%2F%2Fwordpress-test.app.u-pariscite.fr%2Fdiip%2Fwp-content%2Fuploads%2Fsites%2F27%2F2021%2F11%2FDL_for_sequencial_data.pptx&wdOrigin=BROWSELINK” _builder_version=”3.22.1″ border_width_all=”1px” custom_margin=”10px||” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.22.1″][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.22.1″][et_pb_column type=”1_4″ _builder_version=”3.22.1″][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.22.1″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.22.1″][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”3.22.1″][et_pb_row custom_margin=”120px||” _builder_version=”3.22.1″ locked=”off”][et_pb_column type=”4_4″ _builder_version=”3.0.47″][et_pb_divider _builder_version=”3.22.1″][\/et_pb_divider][et_pb_text admin_label=”\u00c0 lire aussi” _builder_version=”3.22.1″ z_index_tablet=”500″]<\/p>\n

Other seminars
<\/span><\/h2>\n

[\/et_pb_text][et_pb_blog posts_number=”4″ include_categories=”67″ show_author=”off” show_date=”off” show_pagination=”off” module_id=”page_type_blog” _builder_version=”3.22.1″ header_level=”h4″ border_width_bottom_fullwidth=”1px” border_color_bottom_fullwidth=”rgba(51,51,51,0.18)” custom_padding=”||50px|” z_index_tablet=”500″][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"

Foula VagenaApril 13, 4 PMonline (zoom)  Abstract Recurrent neural networks (RNNs) are a family of specialized neural networks for processing sequential data. They can scale to much longer sequences than would be practical for networks without sequence-based specialization and most of them can also process sequences of variable length. In this tutorial we will first… Lire la suite Foula Vagena – Deep Learning for Sequential Data: Models and Applications<\/span><\/a><\/p>\n","protected":false},"author":560,"featured_media":2964,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[57,1,67],"tags":[],"class_list":["post-2959","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-57","category-diip","category-seminars-hands-on-workshops"],"_links":{"self":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/2959","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/users\/560"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/comments?post=2959"}],"version-history":[{"count":6,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/2959\/revisions"}],"predecessor-version":[{"id":3086,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/2959\/revisions\/3086"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media\/2964"}],"wp:attachment":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media?parent=2959"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/categories?post=2959"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/tags?post=2959"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}