Category

Research

Snorkel AI emerged from a research project, and we remain closely connected to the research community. Students and professors associated with the Snorkel project continue to publish academic papers that push the field forward, and the Snorkel AI research team integrates the most promising of those ideas into our platform.

Our picks

Image for Getting better performance from foundation models (with less data)
Getting better performance from foundation models (with less data)
Getting better performance from foundation models (with less data)
August 4, 2023
Fred Sala
Image for Snorkel AI researchers present 18 papers at NeurIPS 2023
Snorkel AI researchers present 18 papers at NeurIPS 2023
The Snorkel AI team will present 18 research papers and talks at the 2023 Neural Information Processing Systems (NeurIPS) conference from December 10-16. The Snorkel papers cover a broad range of topics including fairness, semi-supervised learning, large language models (LLMs), and domain-specific models. Snorkel AI is proud of its roots in the research community and endeavors to remain at the forefront
October 31, 2023
Team Snorkel
Image for Long context models in the enterprise: benchmarks and beyond
Long context models in the enterprise: benchmarks and beyond
Snorkel researchers devised a new way to evaluate long context models and address their “lost-in-the-middle” challenges with mediod voting.
June 6, 2024
Amanda Dsouza

All articles on Research

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How Foundation Models bolster programmatic labeling
Snorkel CEO Alex Ratner interviews Mayee Chen about how Liger improves the effectiveness of programmatic labeling through foundation model embeddings.
January 26, 2023
Team Snorkel
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Unmasking Trafficking Risk in Commercial Sex Supply Chains with Machine Learning
Hamsa Bastani presented a summary of her and her co-authors’ ongoing work using machine learning and Snorkel AI’s tools to detect and track activities that are associated with a high risk for global sex trafficking.
January 20, 2023
Team Snorkel
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Prompting and weak supervision to build better, smaller models
Snorkel AI co-founder and CEO Alex Ratner recently interviewed several Snorkel researchers about their published academic papers. In this video, Alex talks with Ryan Smith, Senior Applied Scientist at Snorkel, about the work he did on using foundation models to build compact, deployable, and effective models.
January 19, 2023
Team Snorkel
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FM Summit shows Foundation Model hurdles and potential
Snorkel AI held its Foundation Model Summit Jan 17, bringing together 12 presenters and over 600 attendees at 10 virtual sessions. The event drew registrants from across many sectors, including the tech industry, healthcare, and financial services.
January 18, 2023
Matt Casey
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Contrastive Learning boosts Foundation Model specialization
Snorkel AI co-founder and CEO Alex Ratner talks with Ananya Kumar about the work he did on improving the effectiveness of foundation models by using contrastive learning, image augmentations, and labeled subsamples.
January 13, 2023
Team Snorkel
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Ask Me Anything approach bolsters foundation models
Researcher Simran Arora tells Snorkel CEO Alex Ratner how she improved foundation model effectiveness by using “Ask Me Anything”-style questions.
January 4, 2023
Team Snorkel
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Combining human and artificial intelligence with human-in-the-loop ML | FDCAI
More components in an ML lifecycle are designed to run on autopilot, but some tasks require human-in-the-loop ML, an active research topic that has seen an increasing number of publications in the last 10 years.
December 28, 2022
Team Snorkel
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Seven research papers push foundation model boundaries
The recent debut of ChatGPT astounded the public with the power and speed of foundation models, but their enterprise use remains hampered by adaptation and deployment challenges. In the past year, Snorkel AI has researched several ways to overcome those challenges. 
December 15, 2022
Matt Casey
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Snorkel AI Team presents research at NeurIPS 2022
The Snorkel AI team will present five research papers advancing weak supervision and programmatic labeling at the NeurIPS 2022 conference that started this week.
November 29, 2022
Team Snorkel
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What can Data-Centric AI learn from data & ML engineering?
Databricks’ Chief Technologist: Data-Centric AI can learn from Data Engineering and ML Engineering in five ways: continuous updates, versioning, code-centric deployment, data privatization and actionable monitoring.
November 5, 2022
Team Snorkel
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Improving upon Precision, Recall, and F1 with Gain metrics
This blog post introduces variants of Precision, Recall, and F1 metrics called Precision Gain, Recall Gain, and F1 Gain. The gain variants have desirable properties such as meaningful linear interpolation of PR curves and a universal baseline across tasks. This post explains what these benefits mean for you, how the gain metrics are calculated and outline some examples for intuitive comparison. 
September 8, 2022
Bradley Fowler
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The Future of Data-Centric AI 2022 day 1 highlights
Snorkel AI just hosted the first day of The Future of Data-Centric AI conference 2022. This conference brings together data scientists, ML engineers, and AI leaders to share insights, best practices, and research on how to evolve the ML lifecycle from model-centric to data-centric approaches. This conference takes place over two days with 40+ sessions, 50+ speakers, and thousands of
August 4, 2022
Louis Bouchard
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Clinical entity classification in electronic health records
Research recap: Ontology-driven weak supervision for clinical entity classification in electronic health records (EHRs)  In this post, I have summarized the research published in this academic paper, Ontology-driven weak supervision for clinical entity classification in electronic health records by Jason Fries et al. This paper was published in Nature Communications in 2021.Problem statement Electronic health records (EHR) contain a rich
June 17, 2022
Nazanin Makkinejad
Sharon Li portrayed
Uncovering the unknowns of deep neural networks by Sharon Li
Learning about the challenges and opportunities behind deep neural networks  In this talk, Assistant Professor in Computer Science Sharon Li shares some exciting work about uncovering the unknowns of deep neural networks. She also shares some exciting challenges and opportunities in this domain. If you would like to watch Sharon’s presentation, we have included it below, or you can find
June 8, 2022
Team Snorkel
James Zou portrayed
A data-centric perspective on trustworthy and interpretable AI
The future of data-centric AI talk series In this talk, Assistant Professor of Biomedical Data Science at Stanford University, James Zou, discusses the work he and his team have been doing from a data-centric perspective to trustworthy and interpretable AI. If you would like to watch James’ presentation, we have included it below, or you can find the entire event
June 6, 2022
Team Snorkel