{"id":287,"date":"2023-05-23T01:57:01","date_gmt":"2023-05-22T20:27:01","guid":{"rendered":"https:\/\/aiml.3it.in\/?page_id=287"},"modified":"2024-07-26T10:30:50","modified_gmt":"2024-07-26T05:00:50","slug":"callindustry","status":"publish","type":"page","link":"https:\/\/www.aimlsystems.org\/2024\/callindustry\/","title":{"rendered":"Call for Papers Industry Track"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; custom_padding_last_edited=&#8221;on|phone&#8221; admin_label=&#8221;Header&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;gcid-1bcf785a-50e1-437b-b09f-65567babc1de&#8221; background_image=&#8221;https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/05\/grid-bg-2.png&#8221; background_size=&#8221;initial&#8221; background_position=&#8221;bottom_center&#8221; background_repeat=&#8221;repeat&#8221; custom_padding=&#8221;||22px||false|false&#8221; 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global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span style=\"font-weight: 400;\">Abstract deadline: <span style=\"text-decoration: line-through;\">July 01<\/span> July 10, 2024, 11:59 pm AoE.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Paper submissions due: <span style=\"text-decoration: line-through;\">July 15<\/span> July 22, 2024, 11:59 pm AoE.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_button button_url=&#8221;https:\/\/cmt3.research.microsoft.com\/AIMLSystems2024\/Track\/11\/Submission\/Create&#8221; url_new_window=&#8221;on&#8221; button_text=&#8221;Submit Papers&#8221; button_alignment=&#8221;left&#8221; disabled_on=&#8221;off|off|off&#8221; admin_label=&#8221;Submit Button&#8221; module_id=&#8221;submit_button&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;_initial&#8221; custom_button=&#8221;on&#8221; button_text_size=&#8221;15px&#8221; button_border_width=&#8221;1px&#8221; button_border_radius=&#8221;78px&#8221; button_font=&#8221;Poppins|500||on|||||&#8221; button_icon=&#8221;&#x24;||divi||400&#8243; animation_style=&#8221;fade&#8221; custom_css_main_element=&#8221;  &#8221; global_colors_info=&#8221;{}&#8221; button_bg_color__hover_enabled=&#8221;on|hover&#8221; button_bg_color__hover=&#8221;&#8221; button_bg_enable_color__hover=&#8221;off&#8221; button_bg_color_gradient_stops__hover=&#8221;#2b87da 0%|#0d1c63 100%&#8221; button_bg_use_color_gradient__hover=&#8221;on&#8221;][\/et_pb_button][et_pb_text _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span style=\"font-weight: 400;\">With the rapid growth of industrial and real-life adoption of artificial intelligence (AI) and<br \/>machine learning (ML), a new research area is emerging at their intersection with<br \/>systems design. This area is seeded by the continued growth in data volume, rapid<br \/>increase in size and complexity of predictive models and scale-up supported through<br \/>development of large-scale AI\/ML hardware. We solicit submissions of papers<br \/>describing designs and implementations of solutions and systems for practical tasks at<br \/>the intersection of AI\/ML and computer systems. The primary emphasis is on papers<br \/>that \u200beither solve or advance the understanding of \u200bissues related to deploying learning<br \/>systems in the real world. We also aim to elicit new connections among these diverse<br \/>fields, and identify tools, best practices, and design principles. Papers demonstrating<br \/>\u200bsignificant, verifiable\u200b business and\/or real-world impact as a result of such deployments<br \/>are encouraged.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><span>The use of artificial intelligence (AI)\u2013generated text in an article shall be disclosed in the acknowledgments section of any paper submitted to an IEEE Conference or Periodical. The sections of the paper that use AI-generated text shall have a citation to the AI system used to generate the text<\/span><\/span><\/p>\n<p>[\/et_pb_text][et_pb_button button_url=&#8221;https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/05\/ACM_Latex_Template.zip&#8221; url_new_window=&#8221;on&#8221; button_text=&#8221;DOWNLOAD ACM LATEX TEMPLATE&#8221; button_alignment=&#8221;left&#8221; disabled_on=&#8221;off|off|off&#8221; admin_label=&#8221;Submit Button&#8221; module_id=&#8221;submit_button&#8221; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;_initial&#8221; custom_button=&#8221;on&#8221; button_text_size=&#8221;15px&#8221; button_border_width=&#8221;1px&#8221; button_border_radius=&#8221;78px&#8221; button_font=&#8221;Poppins|500||on|||||&#8221; button_icon=&#8221;&#x24;||divi||400&#8243; animation_style=&#8221;fade&#8221; custom_css_main_element=&#8221;  &#8221; global_colors_info=&#8221;{}&#8221; button_bg_color__hover_enabled=&#8221;on|hover&#8221; button_bg_color__hover=&#8221;&#8221; button_bg_enable_color__hover=&#8221;off&#8221; button_bg_color_gradient_stops__hover=&#8221;#2b87da 0%|#0d1c63 100%&#8221; button_bg_use_color_gradient__hover=&#8221;on&#8221;][\/et_pb_button][et_pb_text disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; text_text_color=&#8221;#2d2d2d&#8221; disabled=&#8221;on&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h3><b data-ogsc=\"\">Camera Ready Submission<\/b><\/h3>\n<ul>\n<li>Authors of accepted papers will<span class=\"x_apple-converted-space\" data-ogsc=\"\">\u00a0<\/span>be allocated 8 pages (including references, appendix, acknowledgements) in<span class=\"x_apple-converted-space\" data-ogsc=\"\">\u00a0<\/span>the conference proceedings. Information regarding formatting and submission of final paper can be found at: <a href=\"https:\/\/www.overleaf.com\/gallery\/tagged\/acm-official\" target=\"_blank\" rel=\"noopener noreferrer\" data-auth=\"NotApplicable\" title=\"https:\/\/www.overleaf.com\/gallery\/tagged\/acm-official\" data-ogsc=\"\" data-linkindex=\"0\">https:\/\/www.overleaf.com\/gallery\/tagged\/acm-official<\/a>.<\/li>\n<li class=\"text-justify pe-4\">The deadline for camera-ready paper submission and copyright form is Sep 29, 2023. The camera ready submission will be through <a href=\"https:\/\/cmt3.research.microsoft.com\/AIMLSystems2023\/\" target=\"_blank\" rel=\"noopener noreferrer\" data-auth=\"NotApplicable\" title=\"https:\/\/cmt3.research.microsoft.com\/AIMLSystems2023\/\" data-ogsc=\"\" data-linkindex=\"1\">https:\/\/cmt3.research.microsoft.com\/AIMLSystems2023\/.<\/a><\/li>\n<li class=\"x_MsoNormal\" data-ogsb=\"white\">At least one author of accepted paper must also complete conference registration by Sep 29, 2023 at full rate (not student\/workshop rate) in order for the paper to be included in the proceedings and program. Registration details are available at: <a href=\"https:\/\/www.aimlsystems.org\/2023\/registration\" target=\"_blank\" rel=\"noopener noreferrer\" data-auth=\"NotApplicable\" title=\"https:\/\/www.aimlsystems.org\/2023\/registration\" data-ogsc=\"\" data-linkindex=\"2\">https:\/\/www.aimlsystems.org\/2023\/registration<\/a>.<span class=\"x_apple-converted-space\" data-ogsc=\"\">\u00a0<\/span><\/li>\n<\/ul>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; text_text_color=&#8221;#FFFFFF&#8221; header_3_font=&#8221;|700|||||||&#8221; custom_margin=&#8221;||8px|||&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h3><strong>DEPLOYED Systems<\/strong><\/h3>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span style=\"font-weight: 400;\">We specially encourage implementation of a system that solves a real-world problem and is (or was or is planned) in production use for an extended period. The paper should present the problem, its significance to the application domain, the decisions and tradeoffs made when making design choices for the solution, the deployment challenges, and the lessons learned from successes and failures (when applicable). Papers that describe enabling infrastructure for large-scale deployment of applied machine learning also fall in this category. The work may particularly focus on how to overcome real challenges in the pipelines which may include data collection, low-resource processing, and usability, and it is perfectly fine that the underlying machine learning algorithms are not fundamentally groundbreaking.<\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][et_pb_column type=&#8221;2_5&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_tabs active_tab_background_color=&#8221;#1c1b3a&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; tab_text_color=&#8221;#FFFFFF&#8221; background_color=&#8221;rgba(0,0,0,0)&#8221; width=&#8221;100%&#8221; border_radii=&#8221;on|11px|11px|11px|11px&#8221; global_colors_info=&#8221;{%22gcid-f1f9244b-c8ab-43e1-95c3-c0bdf69ac7b5%22:%91%22active_tab_background_color%22%93}&#8221;][et_pb_tab title=&#8221;Important Dates&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<ul>\n<li>Abstract deadline: July 10, 2024, 11:59 pm AoE.<\/li>\n<li>Paper submissions due: July 22, 2024, 11:59 pm AoE.<\/li>\n<li>Author notifications: Aug 15, 2024, 11:59 pm AoE.<\/li>\n<li>Camera ready deadline: Aug 31, 2024, 11:59 pm AoE.<\/li>\n<li>Conference dates: October 8-11, 2024<\/li>\n<\/ul>\n<p>[\/et_pb_tab][\/et_pb_tabs][et_pb_tabs active_tab_background_color=&#8221;#1c1b3a&#8221; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; tab_text_color=&#8221;#FFFFFF&#8221; background_color=&#8221;rgba(0,0,0,0)&#8221; border_radii=&#8221;on|11px|11px|11px|11px&#8221; global_colors_info=&#8221;{%22gcid-f1f9244b-c8ab-43e1-95c3-c0bdf69ac7b5%22:%91%22active_tab_background_color%22%93}&#8221;][et_pb_tab title=&#8221;Chairs&#8221; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<ul>\n<li><a href=\"https:\/\/www.linkedin.com\/in\/veenamendiratta\/\" target=\"_blank\" rel=\"noopener\">Veena Mendiratta,<\/a> Northwestern University, USA<\/li>\n<li><a href=\"https:\/\/www.linkedin.com\/in\/anupamisb\/\" target=\"_blank\" rel=\"noopener\">Anupam Purwar<\/a>, Amazon, USA<\/li>\n<li><a href=\"https:\/\/www.linkedin.com\/in\/valeria-tomaselli-42460924\/?originalSubdomain=it\" target=\"_blank\" rel=\"noopener\">Valeria Tomaselli,<\/a> STMicroelectronics, Italy<\/li>\n<\/ul>\n<p>[\/et_pb_tab][\/et_pb_tabs][\/et_pb_column][\/et_pb_row][et_pb_row disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; header_4_font_size_tablet=&#8221;&#8221; header_4_font_size_phone=&#8221;22px&#8221; header_4_font_size_last_edited=&#8221;on|desktop&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h4 id=\"topics-of-interest\">Topics of Interest<\/h4>\n<p>The topics of interest include AI\/ML systems machine learning applications in all mature and emerging domains, as well as contributions to enabling algorithmic, infrastructure, and optimization methodologies to improve learning efficiency, scaling, and adoption\/deployment. The topics include, but are not limited to:<\/p>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_text][et_pb_tabs active_tab_background_color=&#8221;#1c1b3a&#8221; inactive_tab_background_color=&#8221;#0b91c6&#8243; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; tab_text_color=&#8221;#FFFFFF&#8221; background_color=&#8221;rgba(0,0,0,0)&#8221; border_radii=&#8221;on|11px|11px|11px|11px&#8221; global_colors_info=&#8221;{%22gcid-f1f9244b-c8ab-43e1-95c3-c0bdf69ac7b5%22:%91%22active_tab_background_color%22%93}&#8221;][et_pb_tab title=&#8221;AI\/ML&#8221; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<div class=\"text-attention\">\n<ul>\n<li>Efficient model training, inference, and serving.<\/li>\n<li>Distributed and parallel learning algorithms<\/li>\n<li>Privacy and security for ML applications<\/li>\n<li>Testing, debugging, and monitoring of ML applications.<\/li>\n<li>Fairness, interpretability and explainability for ML applications<\/li>\n<li>Data preparation, feature selection, and feature extraction<\/li>\n<li>ML programming models and abstractions<\/li>\n<li>Programming languages for machine learning<\/li>\n<li>Visualization of data, models, and predictions<\/li>\n<li>Specialized hardware for machine learning<\/li>\n<li>Hardware-efficient ML methods<\/li>\n<li>Machine Learning for Systems<\/li>\n<li>Systems for Machine Learning<\/li>\n<li>Lessons learned from end-to-end production ML pipelines.<\/li>\n<li>Emerging practices such as AI-ML Ops<\/li>\n<li>Systems for Generative AI<\/li>\n<li>Generative AI use-cases<\/li>\n<li>\u00a0Benchmarking and performance studies for Generative AI tools<\/li>\n<\/ul>\n<\/div>\n<p>[\/et_pb_tab][\/et_pb_tabs][\/et_pb_column][\/et_pb_row][et_pb_row disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;7px|||||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; background_enable_color=&#8221;off&#8221; custom_margin=&#8221;||14px|||&#8221; animation_direction=&#8221;bottom&#8221; hover_enabled=&#8221;0&#8243; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221; sticky_enabled=&#8221;0&#8243; locked=&#8221;off&#8221;][et_pb_accordion_item title=&#8221;Important note to authors about the new ACM open access publishing model&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.25.1&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#f7f7f7&#8243; background_enable_color=&#8221;on&#8221; hover_enabled=&#8221;0&#8243; global_colors_info=&#8221;{}&#8221; sticky_enabled=&#8221;0&#8243;]<\/p>\n<p>ACM has introduced a new open access publishing model for the International Conference Proceedings Series (ICPS). Authors based at institutions that are not yet part of the <a href=\"https:\/\/libraries.acm.org\/acmopen\/open-participants\" target=\"_blank\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/libraries.acm.org\/acmopen\/open-participants&amp;source=gmail&amp;ust=1721965018718000&amp;usg=AOvVaw08K2raXgm5uGBK4NAjqzgG\" rel=\"noopener\">ACM Open program<\/a> and do not qualify for a waiver will be required to pay an article processing charge (APC) to publish their ICPS article in the ACM Digital Library. To determine whether or not an APC will be applicable to your article, please follow the detailed guidance here: <a href=\"https:\/\/www.acm.org\/publications\/icps\/author-guidance\" target=\"_blank\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/www.acm.org\/publications\/icps\/author-guidance&amp;source=gmail&amp;ust=1721965018718000&amp;usg=AOvVaw3yW8py6g90M47RyskouKNT\" rel=\"noopener\">https:\/\/www.acm.org\/<wbr \/>publications\/icps\/author-<wbr \/>guidance<\/a>.<\/p>\n<p>Further information may be found on the ACM website, as follows:<\/p>\n<p>Full details of the new ICPS publishing model: <a href=\"https:\/\/www.acm.org\/publications\/icps\/faq\" target=\"_blank\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/www.acm.org\/publications\/icps\/faq&amp;source=gmail&amp;ust=1721965018718000&amp;usg=AOvVaw1HKKXkd4ki_HfyAVLEGg8c\" rel=\"noopener\">https:\/\/www.acm.org\/<wbr \/>publications\/icps\/faq<\/a><br \/>Full details of the ACM Open program: <a href=\"https:\/\/www.acm.org\/publications\/openaccess\" target=\"_blank\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/www.acm.org\/publications\/openaccess&amp;source=gmail&amp;ust=1721965018718000&amp;usg=AOvVaw2yL9XalOCin6I5BV91zRH-\" rel=\"noopener\">https:\/\/www.acm.org\/<wbr \/>publications\/openaccess<\/a><\/p>\n<p>Please direct all questions about the new model to <a href=\"mailto:icps-info@acm.org\" target=\"_blank\" rel=\"noopener\">icps-info@acm.org<\/a>.<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_text _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><b>Style and Author Instructions<\/b><\/p>\n<p><span>Regular papers must not exceed 6 pages including bibliography.<br \/>Short papers must not exceed 4 pages including bibliography and will be presented as posters.<br \/>Only electronic submissions in PDF format using the ACM Latex template will be considered. Submissions will be handled through <\/span><a href=\"https:\/\/cmt3.research.microsoft.com\/AIMLSystems2024\/\"><span>https:\/\/cmt3.research.microsoft.com\/AIMLSystems2024\/<\/span><\/a><span>.<\/span><\/p>\n<p><b>\u00a0<\/b>We will accept all papers that meet the high quality and innovation levels required by the AI-ML Systems conference. All accepted papers will appear in the proceedings.<\/p>\n<p><b>\u00a0<\/b>Submission of papers to AI-ML Systems 2024 also carries with it the implied agreement that one or more of the listed authors will register for and attend the conference and present the paper. Papers not presented at the conference will not be included in the final program or in the digital proceedings.<\/p>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_text][et_pb_accordion icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_direction=&#8221;bottom&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; disabled=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;Reviewing process&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#f4f4f4&#8243; background_enable_color=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<ul>\n<li><strong>Paper Format:<\/strong>\u00a0Please prepare your submission using a double-column format. This format allows for optimal readability and consistency throughout the proceedings.<\/li>\n<li><strong>Double-Blind Review:<\/strong> In order to maintain anonymity during the review process, please refrain from including author names and affiliations in the paper. This will help ensure an unbiased evaluation of your work.<\/li>\n<li><strong>Paper Upload:<\/strong> Kindly upload the PDF file of your paper. The maximum file size allowed for submission is 20MB.<\/li>\n<\/ul>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_direction=&#8221;bottom&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; disabled=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;Submission format&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#f4f4f4&#8243; background_enable_color=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span>Research papers must not exceed 6 pages, including any appendix, with an unlimited number of pages containing only bibliography. Only electronic submissions in PDF format using the ACM sigconf template (see\u00a0<\/span><a href=\"https:\/\/www.acm.org\/publications\/proceedings-template\">https:\/\/www.acm.org\/publications\/proceedings-template<\/a><span>) will be considered. The submissions will be through\u00a0<\/span><a href=\"https:\/\/cmt3.research.microsoft.com\/AIMLSystems2023\">https:\/\/cmt3.research.microsoft.com\/AIMLSystems2023<\/a><span>.<\/span><\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; fullwidth=&#8221;on&#8221; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; background_enable_color=&#8221;off&#8221; background_enable_image=&#8221;off&#8221; background_size=&#8221;custom&#8221; background_image_width=&#8221;50%&#8221; background_image_height=&#8221;50%&#8221; background_repeat=&#8221;repeat&#8221; disabled=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; header_4_font_size_tablet=&#8221;&#8221; header_4_font_size_phone=&#8221;22px&#8221; header_4_font_size_last_edited=&#8221;on|desktop&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h4 id=\"topics-of-interest\">Topics of Interest<\/h4>\n<p>The topics of interest include AI\/ML systems machine learning applications in all mature and emerging domains, as well as contributions to enabling algorithmic, infrastructure, and optimization methodologies to improve learning efficiency, scaling, and adoption\/deployment. The topics include, but are not limited to:<\/p>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_text][et_pb_tabs active_tab_background_color=&#8221;#1c1b3a&#8221; inactive_tab_background_color=&#8221;#0b91c6&#8243; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; tab_text_color=&#8221;#FFFFFF&#8221; background_color=&#8221;rgba(0,0,0,0)&#8221; border_radii=&#8221;on|11px|11px|11px|11px&#8221; global_colors_info=&#8221;{%22gcid-f1f9244b-c8ab-43e1-95c3-c0bdf69ac7b5%22:%91%22active_tab_background_color%22%93}&#8221;][et_pb_tab title=&#8221;AI\/ML&#8221; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<div class=\"text-attention\">\n<ul>\n<li>Efficient model training, inference, and serving.<\/li>\n<li>Distributed and parallel learning algorithms<\/li>\n<li>Privacy and security for ML applications<\/li>\n<li>Testing, debugging, and monitoring of ML applications.<\/li>\n<li>Fairness, interpretability and explainability for ML applications<\/li>\n<li>Data preparation, feature selection, and feature extraction<\/li>\n<li>ML programming models and abstractions<\/li>\n<li>Programming languages for machine learning<\/li>\n<li>Visualization of data, models, and predictions<\/li>\n<li>Specialized hardware for machine learning<\/li>\n<li>Hardware-efficient ML methods<\/li>\n<li>Machine Learning for Systems<\/li>\n<li>Systems for Machine Learning<\/li>\n<li>Lessons learned from end-to-end production ML pipelines.<\/li>\n<li>Emerging practices such as AI-ML Ops<\/li>\n<li>Systems for Generative AI<\/li>\n<li>Generative AI use-cases<\/li>\n<li>\u00a0Benchmarking and performance studies for Generative AI tools<\/li>\n<\/ul>\n<\/div>\n<p>[\/et_pb_tab][\/et_pb_tabs][\/et_pb_column][\/et_pb_row][et_pb_row disabled_on=&#8221;off|off|off&#8221; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;7px|||||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><b>Style and Author Instructions<\/b><\/p>\n<p><span>Regular papers must not exceed 6 pages including bibliography.<br \/>Short papers must not exceed 4 pages including bibliography and will be presented as posters.<br \/>Only electronic submissions in PDF format using the ACM Latex template will be considered. Submissions will be handled through <\/span><a href=\"https:\/\/cmt3.research.microsoft.com\/AIMLSystems2024\/\"><span>https:\/\/cmt3.research.microsoft.com\/AIMLSystems2024\/<\/span><\/a><span>.<\/span><\/p>\n<p><b>\u00a0<\/b>We will accept all papers that meet the high quality and innovation levels required by the AI-ML Systems conference. All accepted papers will appear in the proceedings.<\/p>\n<p><b>\u00a0<\/b>Submission of papers to AI-ML Systems 2024 also carries with it the implied agreement that one or more of the listed authors will register for and attend the conference and present the paper. Papers not presented at the conference will not be included in the final program or in the digital proceedings.<\/p>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_text][et_pb_accordion icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_direction=&#8221;bottom&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; disabled=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;Reviewing process&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#f4f4f4&#8243; background_enable_color=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<ul>\n<li><strong>Paper Format:<\/strong>\u00a0Please prepare your submission using a double-column format. This format allows for optimal readability and consistency throughout the proceedings.<\/li>\n<li><strong>Double-Blind Review:<\/strong> In order to maintain anonymity during the review process, please refrain from including author names and affiliations in the paper. This will help ensure an unbiased evaluation of your work.<\/li>\n<li><strong>Paper Upload:<\/strong> Kindly upload the PDF file of your paper. The maximum file size allowed for submission is 20MB.<\/li>\n<\/ul>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_direction=&#8221;bottom&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; disabled=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;Submission format&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#f4f4f4&#8243; background_enable_color=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span>Research papers must not exceed 6 pages, including any appendix, with an unlimited number of pages containing only bibliography. Only electronic submissions in PDF format using the ACM sigconf template (see\u00a0<\/span><a href=\"https:\/\/www.acm.org\/publications\/proceedings-template\">https:\/\/www.acm.org\/publications\/proceedings-template<\/a><span>) will be considered. The submissions will be through\u00a0<\/span><a href=\"https:\/\/cmt3.research.microsoft.com\/AIMLSystems2023\">https:\/\/cmt3.research.microsoft.com\/AIMLSystems2023<\/a><span>.<\/span><\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][et_pb_row disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;7px|||||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;Features&#8221; module_id=&#8221;about&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; background_color=&#8221;#dbdbdb&#8221; background_image=&#8221;https:\/\/www.aimlsystems.org\/2023\/wp-content\/uploads\/2023\/05\/rm380-10.jpg&#8221; background_blend=&#8221;overlay&#8221; custom_padding=&#8221;3.9%|||2px|false|false&#8221; use_background_color_gradient_phone=&#8221;on&#8221; background_color_gradient_stops_phone=&#8221;#001528 0%|rgba(255, 255, 255, 0) 10%|rgba(255,255,255,0) 70%|#0f0122 100%&#8221; locked=&#8221;off&#8221; collapsed=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;27px||43px|||&#8221; disabled=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.23.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h2>Accepted Industry Papers<\/h2>\n<p>[\/et_pb_text][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;1.\tCONRAD: Cognitive Intent Driven 5G Network Slice Planning and Design&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><strong>Authors:<\/strong> Ajay Kattepur (Ericsson)*; Swarup Kumar Mohalik (Ericsson); Ian Burdick (Ericsson); Marin Orlic (Ericsson); Leonid Mokrushin (Ericsson)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;2.\tUncovering Critical Products in Retail Baskets: A Predictive Modelling Approach to Increase Order Fulfilment&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><strong>Authors:<\/strong> Preeti Gopal (Walmart Global Tech)*; Sivaram Prasad Mudunuri (Walmart Global Tech); Sumit Dutta (Walmart Global Tech); Kamiya Motwani (Walmart Global Tech)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;3.\tMaximizing Success Rate of Payment Routing using Non-stationary Bandits&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><strong>Authors:<\/strong> Aayush Chaudhary (Dream11)*; Abhinav Rai (Dream11); Abhishek Gupta (The Ohio State University)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;\t4.\tt-RELOAD: A REinforcement Learning-based Recommendation for Outcome-driven Application&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><strong>Authors:<\/strong> Debanjan Sadhukhan (Games24x7 Pvt Ltd)*; Sachin Kumar (Games 24&#215;7 Pvt Ltd); Swarit Sankule (Games 24&#215;7 Pvt Ltd); Tridib Mukherjee (Games24x7)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;5.\tPhysics Guided Generative Learning for Domain Adaptable Data Synthesis : Progressive Fault Synthesization for Predictive Machine Maintenance&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><strong>Authors:<\/strong> Soma Bandyopadhyay (Tata Consultancy Services)*; Anish Datta (Tata Consultancy Services); Mudassir Ali Sayyed (Fraunhofer Enas); Tapas Chakravarty (Tata Consultancy Services); Arpan Pal (Tata Consultancy Services); Chirabrata Bhaumik (Tata Consultancy Services)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;6.\tSToRM: Smart ticket resolution steps recommendation in facilities management&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><strong>Authors:<\/strong> Rishav Gupta (Walmart Global Tech)*; Abhijeet Pandey (Walmart Global Tech); Abhishek Mishra (Walmart Global Tech)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;7.\tMetric Learning based Shelf Item Recognition on Images from Autonomous Robots&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><strong>Authors:<\/strong> Raghava Balusu (Walmart Global Tech); Lingfeng Zhang (Walmart Global Tech); Abhinav Pachauri (Walmart Global Tech); Han Zhang (Walmart Global Tech); Avinash Jade (Walmart Global Tech); Ashlin Ghosh (Walmart Global Tech); Siddhartha Chakraborty (Walmart Global Tech)*; Zhaoliang Duan (Walmart Global Tech)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;8.\tAdapting Open-Source LLMs for Contract Drafting and Analyzing Multi-Role vs. Single-Role Behavior of ChatGPT for Synthetic Data Generation&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><strong>Authors:<\/strong> Jaykumar Kasundra (Thomson Reuters)*; Shreyans Dhankhar (Thomson Reuters)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;9.\tNoisy Text Data: foible of popular Transformer based NLP models&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><strong>Authors:<\/strong> Kartikay Bagla (Chaos Genius); Shivam Gupta (Ninja Salary); Ankit Kumar (Clearfeed)*; Anuj Gupta (Clearfeed)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][et_pb_row disabled_on=&#8221;on|on|on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;27px||43px|||&#8221; disabled=&#8221;on&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;1.\tAccelerating Causal Algorithms for Industrial-scale Data: A Distributed Computing Approach with Ray Framework&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><strong>Authors:<\/strong> Vishal Verma (Dream11)*; Vinod Reddy (Dream11)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;2.\tFENCE: Fairplay Ensuring Network Chain Entity for Real-Time Multiple ID Detection at Scale In Fantasy Sports&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><strong>Authors:<\/strong> Vishal Verma (Dream11)*; Akriti Upreti (Dream11); Kartavya Kothari (Dream11); Utkarsh Thukral (Dream11)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;3.\tVigil: Effective end-to-end monitoring for large-scale recommender systems at Glance&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><strong>Authors:<\/strong> Priyansh Saxena (Glance)*; Manisha R (Glance)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][et_pb_accordion open_toggle_background_color=&#8221;#f7f7f7&#8243; icon_color=&#8221;#0C71C3&#8243; use_icon_font_size=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;||14px|||&#8221; animation_style=&#8221;slide&#8221; animation_direction=&#8221;bottom&#8221; animation_intensity_slide=&#8221;18%&#8221; border_radii=&#8221;on|30px|30px|30px|30px&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;4.\tMachine Learning Driven Performance Benchmarking for Energy Efficiency &#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.21.0&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><strong>Authors:<\/strong> Mandeep Singh (Walmart Global Tech)*; Viraj Patel (Walmart Global Tech); Ritik Kumar (Walmart Global Tech)<\/p>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Call for Papers Industry Track\u00a0<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"<!-- wp:paragraph -->\n<p>This is an example page. It's different from a blog post because it will stay in one place and will show up in your site navigation (in most themes). Most people start with an About page that introduces them to potential site visitors. It might say something like this:<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:quote -->\n<blockquote class=\"wp-block-quote\"><!-- wp:paragraph -->\n<p>Hi there! I'm a bike messenger by day, aspiring actor by night, and this is my website. I live in Los Angeles, have a great dog named Jack, and I like pi\u00f1a coladas. (And gettin' caught in the rain.)<\/p>\n<!-- \/wp:paragraph --><\/blockquote>\n<!-- \/wp:quote -->\n\n<!-- wp:paragraph -->\n<p>...or something like this:<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:quote -->\n<blockquote class=\"wp-block-quote\"><!-- wp:paragraph -->\n<p>The XYZ Doohickey Company was founded in 1971, and has been providing quality doohickeys to the public ever since. Located in Gotham City, XYZ employs over 2,000 people and does all kinds of awesome things for the Gotham community.<\/p>\n<!-- \/wp:paragraph --><\/blockquote>\n<!-- \/wp:quote -->\n\n<!-- wp:paragraph -->\n<p>As a new WordPress user, you should go to <a href=\"https:\/\/www.aimlsystems.org\/2023\/wp-admin\/\">your dashboard<\/a> to delete this page and create new pages for your content. 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