[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118048-en":3,"doc-seo-118048-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118048,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","DMLR: Data-centric Machine Learning Research - Past, Present and Future","The report outlines the relevance of community engagement and infrastructure development for creating next-generation public datasets that advance machine learning science. It traces DMLR’s key coordinates and objectives, contextualizing its origins and summarizing community activities that support dataset creation and long-term maintenance. The text emphasizes a collective path toward positive scientific, societal, and business impact, and invites readers to contribute as open source contributors, organizers, researchers, or reviewers.","arXiv :2311 . 13028v2 [ cs .LG] 1 Jun 2024  \nDMLR: Data-centric Machine Learning Research  \n-  \nPast, Present and Future  \nLuis Oala1 ∗, Manil Maskey2 , Lilith Bat-Leah3 , Alicia Parrish4 , Nezihe Merve G¨urel5 , Tzu-Sheng Kuo6 , Yang Liu7,8 , Rotem Dror9 , Danilo Brajovic10 , Xiaozhe Yao34 , Max Bartolo11 , William Gaviria Rojas 12 , Ryan Hileman13 , Rainier Aliment4 , Michael W. Mahoney 14,15,16 , Meg Risdal17 , Matthew Lease18 , Wojciech Samek19,20 , Debo Dutta21 , Curtis Northcutt22 , Cody Coleman12 , Braden Hancock23 , Bernard Koch24 , Girmaw Abebe Tadesse25 , Bojan Karlaˇs26 , Ahmed Alaa14 , Adji Bousso Dieng27 , Natasha Noy4 , Vijay Janapa Reddi26 , James Zou28 , Praveen Paritosh29 , Mihaela van der Schaar30 , Kurt Bollacker29 , Lora Aroyo4 , Ce Zhang31,24 , Joaquin Vanschoren32 , Isabelle Guyon4,33,25 , Peter Mattson4,29  \n1 Dotphoton, 2 NASA, 3 Mod Op, 4 Google, 5 TU Delft, 6 Carnegie Mellon University, 7 UC Santa Cruz, 8 ByteDance Research, 9 University of Haifa, 10 Fraunhofer IPA, 11 Cohere, 12 CoactiveAI, 13 Talon, 14 UC Berkeley, 15 ICSI, 16 LBNL, 17Kaggle, 18 UT Austin, 19 TU Berlin, 20 Fraunhofer HHI, 21 Nutanix, 22 Cleanlab, 23 Snorkel AI, 24 University of Chicago, 25 Microsoft AI for Good Lab, 26 Harvard University, 27Princeton University, 28 Stanford University, 29 MLCommons, 30 University of Cambridge, 31 Together, 32 TU Eindhoven, 33 University of Paris-Saclay, 34 ETH Zurich, 35 ChaLearn  \nReviewed on OpenReview: [https: // openreview. net/ forum? id= 2kpu78QdeE](https: // openreview. net/ forum? id= 2kpu78QdeE)  \n[Editor:](Editor: Hongyang Zhang)[ Hongyang Zhang](Editor: Hongyang Zhang)  \nAbstract  \nDrawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will advance machine learning science. We chart a path forward as a collective effort to sustain the creation and maintenance of these datasets and methods towards positive scientific, societal and business impact.  \nKeywords: data-centric machine learning, artificial intelligence, datasets, impact  \n1 Data Ambivalence in Machine Learning  \nWhy state the obvious? Do we really need to emphasize some machine learning (ML) research as data-centric? Hasn’t ML science, at its core, always been just that? After all, designing algorithms that extract models from data is machine learning’s summum bonum. In the pursuit of this goal we often oscillate between two dominant phases: (i) design algorithm and throw data at it, (ii) go back to data (and its intermediate representations)  \n∗. To get involved in the community please join the discord at [https://discord.gg/FswYXMv4j9](https://discord.gg/FswYXMv4j9. For)[. For](https://discord.gg/FswYXMv4j9. For)[ ](https://discord.gg/FswYXMv4j9. For)updates to the manuscript you can contact [luis.oala@dotphoton.com](luis.oala@dotphoton.com).  \n©2024 The DMLR Community.  \nThe DMLR Community  \nFigure 1: A timeline of some inflection points in the development of data-centric ideas.  \nto design better algorithm. This feedback loop informs the ambivalence towards data that many of us will encounter in machine learning practice: on the one hand, we want the algorithm to extract a model from data automatically; on the other hand, we often need to analyze the data and model manually to build good algorithms. Through the lens of this oscillation, data-centric machine learning research (DMLR) can broadly be described as infrastructure, methods and communities revolving around phase (ii) .  \nIn this editorial we outline key coordinates and objectives of DMLR, contextualize its origins, and summarize activities towards growing the DMLR ecosystem. And these lines are also an invitation, a call on you, the reader, to join us in shaping this DMLR future. Be it as open source contributor, community organizer, researcher or reviewer, your ideas and efforts are","cbCaiahisLXyasq7","https://ap.wps.com/l/cbCaiahisLXyasq7","pdf",2147569,1,27,"English","en",105,"# Abstract\n# 1 Data Ambivalence in Machine Learning\n# 2 Past: Data-Centricity Over Time","[{\"question\":\"What does DMLR focus on according to the report?\",\"answer\":\"DMLR centers on infrastructure, methods, and communities that revolve around the data-centric phase of the machine learning workflow, including analysis of data and intermediate representations to build better algorithms.\"},{\"question\":\"How does the report describe the “data ambivalence” in machine learning practice?\",\"answer\":\"It describes an oscillation between designing algorithms and pushing data into them versus going back to data (and model representations) to analyze and refine how good algorithms are built.\"},{\"question\":\"What is the report’s view on how community engagement supports next-generation datasets?\",\"answer\":\"It argues that community engagement and infrastructure development are essential to sustain creation and maintenance of public datasets, enabling positive scientific, societal, and business outcomes.\"}]","DMLR: Data-centric Machine Learning Research - 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