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resources.html
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---
layout: homepage
title: Resources
---
<div class="hero-subheader">
<div class="container">
<div class="mobile-padding">
<h1>{{ page.title }}</h1>
</div>
<div class="row row-spacing mobile-padding">
<div class="light-blue-card-container">
<div class="border-card">
<!-- <p class="subheadline">subheadline</p> -->
<h3>Community</h3>
<p>
For broader Q&A, discussions about using Snorkel, tutorial requests, etc., we have a community forum on
Spectrum.
</p>
<a href="https://spectrum.chat/snorkel?tab=posts" class="btn" target="_blank">Join the Forum</a>
</div>
<div class="border-card">
<!-- <p class="subheadline">subheadline</p> -->
<h3>Mailing List</h3>
<p>
Stay up-to-date about the latest Snorkel-related announcements, releases, and workshops with the mailing
list. We promise not to clutter your inbox :)
</p>
<a href="https://groups.google.com/forum/#!forum/snorkel-ml" class="btn" target="_blank">Sign Up</a>
</div>
<div class="border-card">
<!-- <p class="subheadline">subheadline</p> -->
<h3>GitHub Issues</h3>
<p>
We use GitHub Issues as a place to put bugs and feature requests — anything code-related.
</p>
<a href="https://github.com/snorkel-team/snorkel/issues" class="btn" target="_blank">GitHub</a>
</div>
</div>
</div>
<div class="row vertical-align mobile-padding">
<div class="col-sm-12">
<p class="subheadline">SNORKEL IN THE WILD</p>
<h1>Applications</h1>
</div>
</div>
<div class="row mobile-padding list-test">
<div class="col-sm-5">
<ul class="publications-list">
<li>
Serving >1B queries (multiple languages) with weak supervision and data slicing systems at Apple:
<a href="https://arxiv.org/abs/1909.05372">
Overton: A Data System for Monitoring and Improving Machine-Learned Products
</a>
</li>
<li>
Conversational agents at IBM:
<a href="https://arxiv.org/pdf/1812.06176.pdf">Bootstrapping Conversational Agents With Weak Supervision
(AAAI
2019)</a>
</li>
<li>
Web content & event classification at Google:
<a href="https://arxiv.org/abs/1812.00417">Snorkel DryBell: A Case Study in Deploying Weak Supervision at
Industrial Scale (SIGMOD Industry 2019)</a>, and
<a href="https://ai.googleblog.com/2019/03/harnessing-organizational-knowledge-for.html">Google AI blog
post</a>
</li>
<li>
Business intelligence at Intel:
<a href="https://ajratner.github.io/assets/papers/Osprey_DEEM.pdf">Osprey: Non-Programmer Weak Supervision
of Imbalanced Extraction
Problems (SIGMOD DEEM 2019)</a>
</li>
<li>
Medical device surveillance with electronic health records
<a href="https://arxiv.org/abs/1904.07640">Medical device surveillance with electronic health records
</a>
</li>
<li>
Anti-semitic tweet classification w/ Snorkel + transfer learning:
<a href="https://t.co/h0zGQwDD59">A Technique for Building NLP Classifiers Efficiently with
Transfer Learning and Weak Supervision (Blog post 2019)</a>
</li>
<li>
Catching cheating at Chegg (<a
href="https://www.edsurge.com/news/2018-03-01-cheating-on-chegg-maybe-not-on-its-tutoring-platform">Article</a>)
</li>
</ul>
</div>
<div class="col-sm-2 hidden-xs"></div>
<div class="col-sm-5 mobile-margin">
<ul class="publications-list">
<li>
Medical image triaging at Stanford Radiology:
<a href="https://arxiv.org/abs/1903.11101">Cross-Modal Data Programming Enables Rapid Medical Machine
Learning (Preprint)
</a>
</li>
<li>
GWAS KBC with Stanford Genomics:
<a href="https://www.nature.com/articles/s41467-019-11026-x">A machine-compiled database of genome-wide
association studies
(Nature Communications 2019)</a>
</li>
<li>
Clinical text classification:
<a href="https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-018-0723-6">A clinical text
classification paradigm using weak supervision
and deep representation (BMC MIDM 2019)</a>
</li>
<li>
SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data
<a href="https://arxiv.org/abs/1704.06360">SwellShark: A Generative Model for Biomedical Named Entity
Recognition without Labeled Data
</a>
</li>
<li>
Social media text mining:
<a href="https://ieeexplore.ieee.org/abstract/document/8609589/authors#authors">Deep Text Mining of
Instagram Data without Strong Supervision
(ICWI 2018)</a>
</li>
<li>
Cardiac MRI classification with Stanford Medicine:
<a href="https://www.nature.com/articles/s41467-019-11012-3">Weakly supervised classification of rare aortic
valve
malformations (Nature Communications
2019)</a>
</li>
<li>
Weak Supervision over Autonomous Driving Data with Intel:
<a href="https://ieeexplore.ieee.org/abstract/document/8814147">Utilizing Weak Supervision to Infer Complex
Objects and Situations in Autonomous Driving Data (IEEE Intelligent Vehicles Symposium
2019)</a>
</li>
</ul>
</div>
</div>
<div class="row vertical-align mobile-padding">
<div class="col-sm-12">
<p class="subheadline">PAPERS AND PRE-PRINTS</p>
<h1>Publications</h1>
</div>
</div>
<div class="row mobile-padding list-test">
<div class="col-sm-5">
<ul class="publications-list">
<li>
<a href="https://arxiv.org/abs/1711.10160">Snorkel: Rapid Training Data Creation with Weak Supervision</a>
(VLDB 2018)
</li>
<li>
<a href="https://arxiv.org/abs/1605.07723">Data Programming: Creating Large Training Sets, Quickly</a>
(NeurIPS 2016)
</li>
<li>
<a href="https://cs.stanford.edu/~chrismre/papers/Chris_Re-KDD.pdf">Snorkel and the Software 2.0 vision</a>
(KDD 2018)
</li>
<li>
<a href="https://arxiv.org/abs/1703.00854">Learning the Structure of Generative Models without Labeled
Data</a> (ICML 2017)
</li>
<li>
<a href="https://arxiv.org/pdf/1903.05844.pdf">Learning Dependency Structures for Weak Supervision
Models</a> (ICML 2019)
</li>
<li>
<a href="https://arxiv.org/abs/1810.02840">Training Complex Models with Multi-Task Weak Supervision</a>
(AAAI 2019)
</li>
<li>
<a href="https://arxiv.org/abs/1909.06349">Slice-based Learning: A Programming Model for Residual Learning
in Critical Data Slices
</a>
(NeurIPS 2019)
</li>
<li>
<a href="https://ajratner.github.io/assets/papers/software_2_mmt_vision.pdf">The Role of Massively
Multi-Task and Weak Supervision in Software 2.0</a> (CIDR 2019)
</li>
<li>
<a href="https://ajratner.github.io/assets/papers/ratner-sigmoddemo17.pdf">Snorkel: Fast Training Set
Generation for Information Extraction</a> (SIGMOD DEMO 2017)
</li>
<li>
<a href="https://drive.google.com/file/d/0B8FX-5qN3tbjajJ0RHFWbjhaUUdDVXFDRS1rRHF2YTNPVmtR/view">Interactive
Programmatic Labeling for Weak Supervision</a> (KDD DCCL 2019)
</li>
<li>
<a href="https://arxiv.org/abs/1904.11622">Scene Graph Prediction with Limited Labels</a> (ICCV 2019)
</li>
</ul>
</div>
<div class="col-sm-2 hidden-xs"></div>
<div class="col-sm-5 mobile-margin">
<ul class="publications-list">
<li>
<a href="https://arxiv.org/abs/1709.01643">Learning to Compose Domain-Specific Transformations for Data
Augmentation</a> (NeurIPS 2017)
</li>
<li>
<a href="https://arxiv.org/abs/1709.02477">Inferring Generative Model Structure with Static Analysis</a>
(NeurIPS 2017)
</li>
<li>
<a href="https://arxiv.org/abs/1805.03818">Training Classifiers with Natural Language Explanations</a> (ACL
2018)
</li>
<li>
<a href="http://cs.stanford.edu/people/chrismre/papers/DDL_HILDA_2016.pdf">Data Programming with DDLite:
Putting Humans in a Different Part of the Loop</a> (HILDA @ SIGMOD 2016; note Snorkel was previously
<em>DDLite</em>)
</li>
<li>
<a href="https://arxiv.org/abs/1610.08123">Socratic Learning: Correcting Misspecified Generative Models
using Discriminative Models</a>
</li>
<li>
<a href="https://arxiv.org/abs/1703.05028" target="_blank">Fonduer: Knowledge Base Construction from Richly
Formatted Data</a> (SIGMOD 2018)
</li>
<li>
<a href="http://www.vldb.org/pvldb/vol12/p223-varma.pdf">Snuba: Automating Weak Supervision to Label
Training Data</a> (VLDB 2019)
</li>
<li>
<a href="https://openreview.net/forum?id=r1gPtjcH_N">Improving Sample Complexity with Observational
Supervision</a> (ICLR LLD 2019)
</li>
<li>
<a href="https://arxiv.org/abs/1810.02840">A Kernel Theory of Modern Data Augmentation</a> (ICML 2019)
</li>
</ul>
</div>
</div>
</div>
</div>