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<h1>NeurIPS Data-Centric AI Workshop</h1>
<hr>
<p>
<!-- <a href="https://neurips.cc/Conferences/2021/" target="_blank" style="color:white"><b>At the 35th NeurIPS Conference</b></a><br><br> -->
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<b>14 December 2021</b><br>
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<a href="https://nips.cc/Register" target="_blank" class="btn btn-warning btn-xl">Register</a>
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<p>Data-Centric AI (DCAI) represents the recent transition from focusing on modeling to the underlying data used to train and evaluate models. Increasingly, common model architectures have begun to dominate a wide range of tasks, and predictable scaling rules have emerged. While building and using datasets has been critical to these successes, the endeavor is often artisanal -- painstaking and expensive. The community lacks high productivity and efficient open data engineering tools to make building, maintaining, and evaluating datasets easier, cheaper, and more repeatable. The DCAI movement aims to address this lack of tooling, best practices, and infrastructure for managing data in modern ML systems.</p>
<p>The main objective of this workshop is to cultivate the DCAI community into a vibrant interdisciplinary field that tackles practical data problems. We consider some of those problems to be: data collection/generation, data labeling, data preprocess/augmentation, data quality evaluation, data debt, and data governance. Many of these areas are nascent, and we hope to further their development by knitting them together into a coherent whole. Together we will define the DCAI movement that will shape the future of AI and ML. Please see our call for papers below to take an active role in shaping that future! If you have any questions, please reach out to the organizers (<a href="mailto:[email protected]">[email protected]</a>)
<!-- or ask on Discourse (<a style="color: black"; href=" https://community.deeplearning.ai/invites/pxU2rPjLye">new user</a>)
(<a style="color: black"; href="https://community.deeplearning.ai/c/random-discussions/data-centric-ai/data-centric-ai-workshop/217">existing user</a>) --> </p>
<p>Learn more about Data Centric AI (DCAI)
<!-- <span style="width: 20px; display: inline-block;"></span> -->
<a href="https://www.youtube.com/watch?v=06-AZXmwHjo&ab_channel=DeepLearningAI]" target="_blank">here</a>. This workshop builds on a tradition of series of workshops focusing on the role of data in AI:
<ul>
<li><a href="http://eval.how/dew2020/index.html" target="_blank">Data Excellence</a> @ HCOMP</li>
<li><a href="http://eval.how/aaai-2020/" target="_blank">Meta-Eval 2020</a> @ AAAI</li>
<li><a href="http://eval.how/aaai/" target="_blank">REAIS 2019</a> @ HCOMP</li>
<li><a href="https://sadworkshop.wordpress.com/" target="_blank">SAD 2019</a> @ TheWebConf (WWW)</li>
<li><a href="https://sadworkshop.wordpress.com/2018-edition/" target="_blank">SAD 2018</a> @ HCOMP</li>
</ul>
</p>
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<h2 class="section-heading">Important Dates</h2>
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<h3>Early Submission Deadline</h3>
<p class="text-muted">September 17, 2021</p>
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<h3>Submission Deadline</h3>
<p class="text-muted">September 30, 2021</p>
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<h3>Notification of acceptance</h3>
<p class="text-muted">October 22, 2021</p>
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<h3>Workshop</h3>
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December 14, 2021<br>
<!-- PT: 6:00am-12:20am<br>
EST: 9:00am-3:20pm<br>
CET: 2:00pm-8:20pm -->
</p>
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<h3>FAQ</h3>
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For questions please check <a href="https://groups.google.com/u/1/g/neurips-data-centric-ai" target="_blank">FAQ</a><br>
<!-- PT: 6:00am-12:20am<br>
EST: 9:00am-3:20pm<br>
CET: 2:00pm-8:20pm -->
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<section class="bg-primary" id="call-for-papers">
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<h2 class="section-heading text-center">Call for Papers</h2>
<hr class="light">
<p class="text-faded">
<p class="text-faded">The ML community has a strong track record of <span style="color: white; font-weight: bold">building and using datasets for AI systems</span>.
But this endeavor is often artisanal—painstaking and expensive.
The community lacks high productivity and efficient open data engineering tools to make building, maintaining and evaluating datasets easier, cheaper and more repeatable.
So, the core challenge is to accelerate dataset creation and iteration together with increasing the efficiency of use and reuse by democratizing data engineering and evaluation. <br><br>
If 80 percent of machine learning work is data preparation, then <span style="color: white; font-weight: bold">ensuring data quality</span> is the most important work of a machine learning team and therefore a vital research area.
Human-labeled data has increasingly become the fuel and compass of AI-based software systems - yet innovative efforts have mostly focused on models and code.
The growing focus on scale, speed, and cost of building and improving datasets has resulted in an impact on quality, which is nebulous and often circularly defined, since the annotators are the source of data and ground truth [Riezler, 2014].
The development of tools to make repeatable and systematic adjustments to datasets has also lagged.
While dataset quality is still the top concern everyone has, the ways in which that is measured in practice is poorly understood and sometimes simply wrong.
A decade later, we see some cause for concern: fairness and bias issues in labeled datasets [Goel and Faltings, 2019], quality issues in datasets [Crawford and Paglen, 2019],
limitations of benchmarks [Kovaleva et al., 2019, Welty et al., 2019] reproducibility concerns in machine learning research [Pineau et al., 2018, Gunderson and Kjensmo, 2018],
lack of documentation and replication of data [Katsuno et al., 2019], and unrealistic performance metrics [Bernstein 2021].<br><br>
We need a framework for <span style="color: white; font-weight: bold">excellence in data engineering</span> that does not yet exist. In the first to market rush with data, aspects of
maintainability, reproducibility, reliability, validity, and fidelity of datasets are often overlooked. We want to turn this way of thinking on its head and highlight examples,
case-studies, methodologies for excellence in data collection. Building an active research community focused on Data Centric AI is an important part of the process of defining
the core problems and creating ways to measure progress in machine learning through data quality tasks. <br></p>
<h3 class="section-heading text-center" id="topics">Submission Instructions</h3>
<p class="text-faded"> We welcome <span style="color: white; font-weight: bold">short papers (1-2 pages)</span> and <span style="color: white; font-weight: bold">long papers (4 pages)</span> addressing one or more of the topics of interest below. All papers need to be formatted according to the <a href="https://nips.cc/Conferences/2021/CallForPapers" target="_blank" style="color:white">NeurIPS2021 Formatting Instructions</a>. Papers will be peer-reviewed by the program committee and accepted papers will be presented as lightning talks during the workshop. If you have any questions about submission, please first check the FAQ link below. Contact us per email only if your question is not answered in the FAQ below, or if you experience any problems with the submission site, please email us at
(<a style="color: white"; href="mailto:[email protected]">[email protected]</a>)
<!-- or ask on Discourse (<a style="color: white"; href=" https://community.deeplearning.ai/invites/pxU2rPjLye">new user</a>)
(<a style="color: white"; href="https://community.deeplearning.ai/c/random-discussions/data-centric-ai/data-centric-ai-workshop/217">existing user</a>) --> </p>
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<br>
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<br>
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<p>
<h3 class="section-heading text-center" id="topics">Topics of Interest</h3></p>
<hr class="primary">
<p class="text-faded">
Data Centric AI workshop is inviting <span style="color: white; font-weight: bold">position papers</span> from researchers and practitioners on topics that include but not limited to the following:
<p class="text-faded"><span style="color: white; font-weight: bold">New Datasets in areas</span>:
<ul class="text-faded">
<li>Speech, vision, manufacturing, medical, recommendation/personalization</li>
<li>Science: <a href="https://www.mgi.gov/" target="_blank" style="color: white">https://www.mgi.gov/</a></li></ul> </p>
<p class="text-faded"><span style="color: white; font-weight: bold">Tools & methodologies </span>for accelerating open-source dataset iteration:
<ul class="text-faded">
<li>Tools that quantify and accelerate time to source and prepare high quality data</li>
<li>Tools that ensure that the data is labeled consistently, such as label consensus</li>
<li>Tools that make improving data quality more systematic</li>
<li>Tools that automate the creation of high quality supervised learning training data from low quality resources, such as forced alignment in speech recognition</li>
<li>Tools that produce consistent and low noise data samples, or remove labeling noise or inconsistencies from existing data</li>
<li>Tools for controlling what goes into the dataset and for making high level edits efficiently to very large datasets, e.g. adding new words, languages, or accents to speech datasets with thousands of hours</li>
<li>Search methods for finding suitably licensed datasets based on public resources </li>
<li>Tools for creating training datasets for small data problems, or for rare classes in the long tail of big data problems </li>
<li>Tools for timely incorporation of feedback from production systems into datasets</li>
<li>Tools for understanding dataset coverage of important classes, and editing them to cover newly identified important cases</li>
<li>Dataset importers that allow easy combination and composition of existing datasets</li>
<li>Dataset exporters that make the data consumable for models and interface with model training and inference systems such as webdataset.</li>
<li>System architectures and interfaces that enable composition of dataset tools such as, MLCube, Docker, Airflow</li>
</ul>
</p>
<p class="text-faded"><span style="color: white; font-weight: bold">Algorithms for working with limited labeled data and improving label efficiency</span>:
<ul class="text-faded">
<li>Data selection techniques such as active learning and core-set selection for identifying the most valuable examples to label.</li>
<li>Semi-supervised learning, few-shot learning, and weak supervision methods for maximizing the power of limited labeled data.</li>
<li>Transfer learning and self-supervised learning approaches for developing powerful representations that can be used for many downstream tasks with limited labeled data. </li>
<li>Novelty and drift detection to identify when more data needs to be labeled.</li>
</ul>
</p>
<p class="text-faded"><span style="color: white; font-weight: bold">Responsible AI development
:</span>
<ul class="text-faded">
<li>Fairness, bias, diversity evaluation and analysis for data sets and modeling/algorithms</li>
<li>Tools for green AI hardware-software system design and evaluation</li>
<li>Scalable, reliable training methods and systems</li>
<li>Tools, methodologies, and techniques for private, secure machine learning training</li>
<li>Efforts toward reproducible AI, such as data cards, model cards</li>
</ul>
</p>
</p>
</div>
</div>
</div>
</section>
<section id="organizers">
<div class="container">
<div class="row">
<div class="col-lg-8 col-lg-offset-2 text-center">
<h2 class="section-heading">Organizing Committee</h2>
<hr class="light">
<p>
<a href="https://www.linkedin.com/in/andrewyng/">Andrew Ng</a>, Landing AI, DeepLearning.AI<br>
<a href="https://lora-aroyo.org/">Lora Aroyo</a>, Google Research<br>
<a href="http://www.codycoleman.com/">Cody Coleman</a>, Stanford University<br>
<a href="https://www.linkedin.com/in/gregory-diamos-1a8b9083/">Greg Diamos</a>, Landing AI<br>
<a href="https://scholar.harvard.edu/vijay-janapa-reddi/home">Vijay Janapa Reddi</a>, Harvard University<br>
<a href="https://joaquinvanschoren.github.io/home/">Joaquin Vanschoren</a>, Eindhoven University of Technology<br>
<a href="https://ai.facebook.com/people/carole-jean-wu/">Carole-Jean Wu</a>, Facebook<br>
<a href="https://sharonzhou.me/">Sharon Zhou</a>, Stanford University<br>
</p>
<br>
<br>
<!-- <h2 class="section-heading">Program Committee</h2>
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TDB
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<section class="bg-primary" style="padding-bottom: 0" id="program">
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<div class="col-lg-8 col-lg-offset-2">
<h2 class="section-heading text-center">Program</h2>
<hr class="primary">
<p>
<h3 class="text-center">Morning Session Schedule</h3>
<br>
<table class="table table-striped text-center" style="font-size: 120%">
<thead style="font-weight: bold">
<tr>
<td>PST</td>
<td>EST</td>
<td>UTC</td>
<td width="50%">Agenda</td>
</tr>
</thead>
<tbody>
<tr>
<td>8:30 AM</td>
<td>11:30 AM</td>
<td>4:30 PM</td>
<td><b>Andrew Ng - Opening Remarks</b></td>
</tr>
<tr>
<td>8:45 AM</td>
<td>11:45 AM</td>
<td>4:45 PM</td>
<td><b>Lora Aroyo - Workshop Overview </b></td>
</tr>
<tr>
<td>9:00 AM</td>
<td>12:00 PM</td>
<td>5:00 PM</td>
<td><b>Keynote: Michael Bernstein - HCI and Crowdsourcing for DCAI</b></td>
</tr>
<tr>
<td>9:15 AM</td>
<td>12:15 PM</td>
<td>5:15 PM</td>
<td><b>Invited Talk: Past/Future of data centric AI with Olga Russakovsky </b></td>
</tr>
<tr>
<td>9:25 AM</td>
<td>12:25 PM</td>
<td>5:25 PM</td>
<td>Lightning Talks: Benchmarking</td>
</tr>
<tr>
<td>10:25 AM</td>
<td>1:25 PM</td>
<td>6:25 PM</td>
<td>Invited Talk: Peter Mattson - DataPerf - Benchmarking Data Centric AI</td>
</tr>
<tr>
<td>10:40 AM</td>
<td>1:40 PM</td>
<td>6:40 PM</td>
<td>Lightning Talks: Theory and Challenge Problems in Data Centric AI</td>
</tr>
<tr>
<td>11:20 AM</td>
<td>2:20 PM</td>
<td>7:20 PM</td>
<td>Invited Talk: Douwe Kiela - FAIR Dynabench</td>
</tr>
<tr>
<td>11:30 AM</td>
<td>2:30 PM</td>
<td>7:30 PM</td>
<td>Lightning Talks: Responsibility and Ethics</td>
</tr>
<tr>
<td>12:10 PM</td>
<td>3:10 PM</td>
<td>8:10 PM</td>
<td>Q&A Panel with Morning Speakers</td>
</tr>
<tr>
<td>12:50 PM</td>
<td>3:50 PM</td>
<td>8:50 PM</td>
<td>Break to watch video recordings</td>
</tr>
</tbody>
</table>
</p>
<br>
<p>
<h3 class="text-center">Afternoon Session Schedule</h3>
<br>
<table class="table table-striped text-center" style="font-size: 120%">
<thead style="font-weight: bold">
<tr>
<td>PST</td>
<td>EST</td>
<td>UTC</td>
<td width="50%">Agenda</td>
</tr>
</thead>
<tbody>
<tr>
<td>1:20 PM</td>
<td>4:20 PM</td>
<td>9:20 PM</td>
<td>Keynote: Alex Ratner & Chris Ré - The Future of Data Centric AI</td>
</tr>
<tr>
<td>1:35 PM</td>
<td>4:35 PM</td>
<td>9:35 PM</td>
<td>Invited Talk: D Sculley - Data Debt</td>
</tr>
<tr>
<td>1:45 PM</td>
<td>4:45 PM</td>
<td>9:45 PM</td>
<td>Lightning Talks: Datasets and Data Synthesis</td>
</tr>
<tr>
<td>2:45 PM</td>
<td>5:45 PM</td>
<td>10:45 PM</td>
<td>Invited Talk: Curtis Northcutt</td>
</tr>
<tr>
<td>2:55 PM</td>
<td>5:55 PM</td>
<td>10:55 PM</td>
<td>Lightning Talks: Data Quality and Iteration</td>
</tr>
<tr>
<td>3:40 PM</td>
<td>6:40 PM</td>
<td>11:40 PM</td>
<td>Invited Talk: Anima Anandkumar</td>
</tr>
<tr>
<td>3:50 PM</td>
<td>6:50 PM</td>
<td>11:50 PM</td>
<td>Lightning Talks: Data Labeling</td>
</tr>
<tr>
<td>4:30 PM</td>
<td>7:30 PM</td>
<td>12:30 AM</td>
<td>Q&A Panel session with afternoon speakers</td>
</tr>
<tr>
<td>5:10 PM</td>
<td>8:10 PM</td>
<td>1:10 AM</td>
<td>Break to watch video recordings/td>
</tr>
</tbody>
</table>
</p>
<br>
<span style="width: 20px; display: inline-block;"></span>
<a href="https://neurips.cc/Conferences/2021/Schedule?showEvent=21860" class="btn btn-default btn-xl page-scroll">Detailed Schedule and Accepted Papers</a>
<!--
<h3 id="papers" class="text-center">Accepted Papers</h3><br>
<ul style="font-size: 120%; color: white">
<li>><br><br></li>
<li><br><br></li>
<li>><br><br></li>
</ul>
-->
<br>
<h3 id="invited-talks" class="text-center">Invited Talks</h3><br>
</div>
</div>
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Michael Bernstein
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Stanford University
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Olga Russakovsky
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Princeton
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Peter Mattson
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Google Brain
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Douwe Kiela
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Facebook AI Research
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Praveen Paritosh
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Google Research
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Stanford AI Lab
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Anima Anandkumar
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NVIDIA
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D Sculley
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Curtis Northcutt
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ChipBrain
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</section>
<section class="bg-primary" style="padding-bottom: 0" id="papers">
<div class="container">
<div class="row">
<div class="col-lg-8 col-lg-offset-2">
<h2 class="section-heading text-center">Accepted Papers</h2>
<hr class="primary">
<p>
<table class="table table-striped text-center" style="font-size: 120%">
<thead style="font-weight: bold">
<tr>
<td width="50%">Title</td>
<td>Authors (* corresponding) </td>
<td>Link</td>
</tr>
</thead>
<tbody>
<tr>
<td>A Hybrid Bayesian Model to Analyse Healthcare Data</td>
<td>Pourshahrokhi, Narges*; Kouchaki, Samaneh; Kober, Kord; Miaskowski, Christine ; Barnaghi, Payam</td>
<td><a href="papers/4_CameraReady_NeurIPS_data_centric(6).pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus</td>
<td>Park, Chanjun*; Lee, Seolhwa; Moon, Hyeonseok; Eo, Sugyeong; Seo, Jaehyung; Lim, Heuiseok</td>
<td><a href="papers/6_CameraReady_DCAI_HighQuality.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>A New Tool for Efficiently Generating Quality Estimation Datasets</td>
<td>Eo, Sugyeong; Park, Chanjun*; Seo, Jaehyung; Moon, Hyeonseok; Lim, Heuiseok</td>
<td><a href="papers/11_CameraReady_DCAI_QE.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Automatic Knowledge Augmentation for Generative Commonsense Reasoning</td>
<td>Seo, Jaehyung*; Park, Chanjun; Eo, Sugyeong; Moon, Hyeonseok; Lim, Heuiseok</td>
<td><a href="papers/14_CameraReady_DCAI_CommonGen.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Tabular Engineering with Automunge</td>
<td>Teague, Nicholas*</td>
<td><a href="papers/15_CameraReady_TabularEngineering_102621_Final.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>A Probabilistic Framework for Knowledge GraphData Augmentation</td>
<td>Chauhan, Jatin*; Gupta, Priyanshu; Minervini, Pasquale</td>
<td><a href="papers/16_CameraReady_KG_Aug_NeurIPS_Workshop.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>FedHist: A Federated-First Dataset for Learning inHealthcare</td>
<td>Khan, Usmann*</td>
<td></td>
</tr>
<tr>
<td>A First Look Towards One-Shot Object Detection with SPOT for Data-Efficient Learning</td>
<td>Chakraborty, Ria*; Popli, Madhur; Lamba, Rachit; Verma, Rishi</td>
<td><a href="papers/18_CameraReady_SCIPUB-5209.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>YMIR: A Rapid Data-centric Development Platform for Vision Applications</td>
<td>Huang, Phoenix X.; Hu, Wenze*; Brendel, William; Chandraker, Manmohan; Li, Li-Jia; Wang, Xiaoyu</td>
<td><a href="papers/19_CameraReady_PAMIR(4).pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Towards better data discovery and collection with flow-based programming</td>
<td>Paleyes, Andrei*; Cabrera, Christian; Lawrence, Neil D</td>
<td><a href="papers/20_CameraReady_dcai_2021_camera_ready.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>CircleNLU: A Tool for building Data-Driven Natural Language Understanding System</td>
<td>Hoang, Vu*</td>
<td><a href="papers/22_CameraReady_NeuRIPS_Data_Centric_AI.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Using Synthetic Images To Uncover Population Biases In Facial Landmarks Detection</td>
<td>Shadmi, Ran*; Laserson, Jonathan; Elbaz, Gil</td>
<td></td>
</tr>
<tr>
<td>Challenges of Working with Materials R&D Data</td>
<td>Kubie, Lenore*; Kroenlein, Kenneth</td>
<td></td>
</tr>
<tr>
<td>PyHard: a novel tool for generating hardness embeddings to support data-centric analysis</td>
<td>Paiva, Pedro Yuri Arbs*; Smith-Miles, Kate; Valeriano, Maria; Lorena, Ana</td>
<td><a href="papers/30_CameraReady_DCAI_2021_camera_ready.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>AirSAS: Controlled Dataset Generation for Physics-Informed Machine Learning</td>
<td>Cowen, Benjamin*; Park, J. Daniel; Blanford, Thomas E.; Goehle, Geoff; Brown, Daniel C.</td>
<td><a href="papers/32_CameraReady_airsas-dcai21.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Open-Sourcing Generative Models for Data-driven Robot Simulations</td>
<td>Bamani, Eran*; Sintov, Avishai; Azulay, Osher; Gurevich, Anton</td>
<td></td>
</tr>
<tr>
<td>Few-Shot Image Classification Challenge On-Board OPS-SAT</td>
<td>Derksen, Dawa*; Meoni, Gabriele; Lecuyer, Gurvan; Mergy, Anne; Maertens, Marcus; Izzo, Dario</td>
<td><a href="papers/37_CameraReady_ops_sat_dataset_centric_competition.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Dialectal Voice : An Open-Source Voice Dataset and Automatic Speech Recognition model for Moroccan Arabic dialectal</td>
<td>Allak, Anass*; Naira, Abdou Mohamed; Imade, Benelallam; Kamel, Gaanoun</td>
<td><a href="papers/42_CameraReady_Dialectal_Voice___An_Open_Source_Voice_Dataset_and_Automatic_Speech_Recognition_model_for_Moroccan_Arabic_dialectal(1).pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>DAG Card is the new Model Card</td>
<td>Tagliabue, Jacopo*; Tuulos, Ville; Greco, Ciro; Dave, Valay</td>
<td><a href="papers/43_CameraReady_neurips_data_centric_2021_DAG_CARDS_camera_ready.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>SCIMAT: Science and Mathematics Dataset</td>
<td>Kollepara, Neeraj; Chatakonda, Snehith K; kumar, pawan*</td>
<td><a href="papers/45_CameraReady_paper_45_update.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Towards Systematic Evaluation in Machine Learning through Automated Stress Test Creation</td>
<td>Madras, David*; Zemel, Richard</td>
<td></td>
</tr>
<tr>
<td>Annotation Quality Framework - Accuracy,Credibility, and Consistency</td>
<td>Lavitas, Liliya*; Lee, Allen; Redfield, Olivia; Fletcher, Daniel; Eck, Matthias; Janardhanan, Sunil</td>
<td><a href="papers/49_CameraReady_DCAI2021_tex(8).pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Ontolabeling: Re-Thinking Data Labeling For Computer Vision</td>
<td>Croce, Nicola*; Nieto, Marcos</td>
<td><a href="papers/50_CameraReady_final_NeurIPS_2021___Data_Centric_AI_Paper.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Natural Adversarial Objects</td>
<td>Lau, Felix*; Harrison, Sasha; Subramani, Nishant; Kim, Aerin; Branson, Elliot R; Liu, Rosanne</td>
<td></td>
</tr>
<tr>
<td>No News is Good News: A Critique of the One Billion Word Benchmark</td>
<td>Ngo, Helen*; Frosst, Nicholas; Madeira Araújo, João G; Hui, Jeff</td>
<td></td>
</tr>
<tr>
<td>A Data-Centric Approach for Training Deep Neural Networks with Less Data</td>
<td>Motamedi, Mohammad*; Sakharnykh, Nikolay; Kaldewey, Tim</td>
<td><a href="papers/56_CameraReady_neurips_2021.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Finding Label Errors in Autonomous Vehicle Data With Learned Observation Assertions</td>
<td>Kang, Daniel*; Arechiga, Nikos; Pillai, Sudeep; Bailis, Peter D; Zaharia, Matei</td>
<td><a href="papers/57_CameraReady_main.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Single-Click 3D Object Annotation on LiDAR Point Clouds</td>
<td>Nguyen, Trung Duc*; Hua, Binh-Son; Nguyen, Thanh; Phung, Dinh</td>
<td><a href="papers/58_CameraReady_camera_ready_v2.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Decreasing Annotation Burden of Pairwise Comparisons with Human-in-the-Loop Sorting: Application in Medical Image Artifact Rating</td>
<td>Jang, Ikbeom*; Danley, Garrison; Chang, Ken; Kalpathy-Cramer, Jayashree</td>
<td><a href="papers/59_CameraReady_manuscript_20211120_cameraready.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation</td>
<td>Jain, Saahil*; Smit, Akshay; Ng, Andrew; Rajpurkar, Pranav</td>
<td><a href="papers/61_CameraReady_DCAI_2021_submission.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>A Data-Centric Image Classification Benchmark</td>
<td>Schmarje, Lars*; Liao, Yuan-Hong; Koch, Reinhard</td>
<td><a href="papers/64_CameraReady_camera-readydcaiv2.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Diagnosing severity levels of Autism Spectrum Disorder with Machine Learning</td>
<td>Cinque, Marcello; Moscato, Vincenzo; Postiglione, Marco*; Riccio, Maria Pia</td>
<td><a href="papers/69_CameraReady__DataCentricAI____Autism.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Sampling To Improve Predictions For Underrepresented Observations In Imbalanced Data</td>
<td>Kjærsgaard, Rune D.*; Grønberg, Manja; Clemmensen, Line</td>
<td><a href="papers/70_CameraReady_DCAI_Sampling_Camera.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Automatic Data Quality Evaluation for Text Classification</td>
<td>li, jiazheng*</td>
<td><a href="papers/72_CameraReady_li_jiazheng_data_centric_ai_workshop.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Building Legal Datasets</td>
<td>Soh, Jerrold*</td>
<td><a href="papers/74_CameraReady_building-legal-datasets-CamReady.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Comparing Data Augmentation and Annotation Standardization to Improve End-to-end Spoken Language Understanding Models</td>
<td>Nicolich-Henkin, Leah*; Nakatani, Taichi; Trozenski, Zach; Whiteman, Joel; Susanj, Nathan</td>
<td><a href="papers/75_CameraReady_comparing_data_augmentation_and_annotation_standardization.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>DiagnosisQA: A semi-automated pipeline for developing clinician validated diagnosis specific QA datasets.</td>
<td>Mishra, Shreya; Awasthi, Raghav; Papay, Frankie; Maheshwari, Kamal; Cywinski, Jacek; Khanna, Ashish; Mathur, Piyush *</td>
<td><a href="papers/76_CameraReady_DCAI_CameraReady_Submission.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Influence of human-expert labels on a neonatal seizure detector based on a convolutional neural network</td>
<td>Borovac, Ana*; Runarsson, Thomas P; Guðmundsson, Steinn; Thorvardsson, Gardar</td>
<td><a href="papers/77_CameraReady_borovac2021influence.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Feminist Curation of Text for Data-centric AI</td>
<td>Bartl, Marion*; Leavy, Susan</td>
<td><a href="papers/79_CameraReady_DCAI_Workshop_NeurIPS_2021_final.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Challenges and Solutions to build a Data Pipeline to Identify Anomalies in Enterprise System Performance</td>
<td>Huang, Xiaobo*; Banerjee, Amitabha; Chen, Chien-Chia; Huang, Chengzhi; Chuang, Tzu Yi; Srivastava, Abhishek; Cheveresan, Razvan</td>
<td></td>
</tr>
<tr>
<td>Human-inspired Data Centric Computer Vision</td>
<td>Tsutsui, Satoshi*; Crandall, David; Yu, Chen</td>
<td><a href="papers/82_CameraReady_2021_Neurips_WS_datacentric_AI(1).pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Utilizing Driving Context to Increase the Annotation Efficiency of Imbalanced Gaze Image Data</td>
<td>Rehm, Johannes*; Gundersen, Odd Erik; Bach, Kerstin; Reshodko, Irina</td>
<td><a href="papers/83_CameraReady_DataCentricAIWorkshop_final.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Unleashing the Power of Industrial Big Data through Scalable Manual Labeling</td>
<td>Paes Leao, Bruno*; Fradkin, Dmitriy; Lan, Tu; Wang, Jianhui</td>
<td><a href="papers/84_CameraReady_MindSynchro___NeurIPS_Data_Centric_AIfinal.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>nferX: a case study on data-centric NLP in biomedicine</td>
<td>Chang, David*; Mathew, Vineet; Kogler, Lorenzo; Jin, Roger; Rao, Krishna; Raghunathan, Bharathwaj; Ip, Wui; Doctor, Zainab; Pawlowski, Colin; Rajesekharan, Ajit</td>
<td><a href="papers/85_CameraReady_NEURIPS_DCAI_2021_Submission_camera_ready.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>On Data-centric Myths</td>
<td>Marcu, Antonia*; Prugel-Bennett, Adam</td>
<td><a href="papers/86_CameraReady_Data_centric_AI_workshop(2).pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>All in one Data Cleansing Tool</td>
<td>Sairaman, Sri Aravind*; Vailoppilly, Arun Prasad ; Sakthivel, Ramkumar; Kumar, Resham Sundar; BDSV, Vignesh; G, Aravind</td>
<td><a href="papers/87_CameraReady_Camera_Ready_Version_Final_Draft_DCAI.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Contrasting the Profiles of Easy and Hard Observations in a Dataset</td>
<td>Moreno, Camila C*; Paiva, Pedro; Nunes, Gustavo; Lorena, Ana</td>
<td><a href="papers/88_CameraReady_Neurips_2021.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>A concept for fitness-for-use evaluation in Machine Learning pipelines</td>
<td>Jonietz, David*</td>
<td><a href="papers/89_CameraReady_data_centric_AI_camera_ready.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Vietnamese Speech-based Question Answering over Car Manuals</td>
<td>Vo, Tin Duy*; Luong, Manh; Minh Le, Duong; Tran, Hieu Minh; Do, Nhan; Nguyen, Duy; Nguyen, Thien; Bui, Hung; Nguyen, Dat Quoc; Phung, Dinh</td>
<td></td>
</tr>
<tr>
<td>Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation</td>
<td>Bai, Haoping*; Cao, Meng; Huang, Ping; Shan, Jiulong</td>
<td><a href="papers/91_CameraReady_Self_supervised_Semi_supervised_Learning_for_Data_Labeling_and_Quality_Evaluation.pdf" style="color:white" > Link </a></td>
</tr>
<tr>
<td>Towards a Taxonomy of Graph Learning Datasets</td>
<td>Liu, Renming; Cantürk, Semih; Wenkel, Frederik; Sandfelder, Dylan; Kreuzer, Devin; Little, Anna; McGuire, Sarah; Perlmutter, Michael; O'Bray, Leslie; Rieck, Bastian; Hirn, Matthew; Wolf, Guy; Rampášek, Ladislav*</td>
<td><a href="papers/92_CameraReady_DCAI_NeurIPS_2021_GTaxonomy.pdf" style="color:white" > Link </a></td>