Weak Supervision#

  • Weak Supervision is the Data Centric AI technique to

Reduce the efforts of manual labeling while unlocking the vast knowledge of domain subject matter experts (SMEs) by leveraging a diversity of weaker, often programmatic supervision sources.

No Training Data? No Problem! Weak Supervision to the Rescue!#

  • Presentation at Quantum Black Meetup at Singapore on May 19, 2022: Link to slides

  • The topics I covered are,

    • ML’s insatiable need for large datasets

    • Contemporary ML leaving out domain knowledge from Subject Matter Experts

    • How Weak Supervision, an approach of Data-Centric AI, solves both the problems simultaneously by encoding domain subject matter expertise into programmatic labeling functions.

    • The WRENCH benchmark to compare various weak supervision algorithms on several standard datasets.

    • Snorkel to combine the various labeling functions.

    • COSINE to fine-tune a final transformer based model that overcomes the noise in weak labels

    • Future Directions and Resources

  • The slides have several links to all the necessary resources if you’re interested to read more.

  • Feel free to use the slides but please remember to credit me with a link back to this repository or my Linkedin profile!

#datascience #machinelearning #artificialintelligence #nlp #algorithms

WeaSEL: Weakly Supervised End-to-end Learning#

An interesting paper/code for those of us working on problems that have low/no labels but high SME domain knowledge. WeaSEL: Weakly Supervised End-to-end Learning.

Train your favorite neural network for weakly-supervised classification:

✅ With only labeling functions (LFs), i.e. without any labeled training data!

✅ In an end-to-end manner, i.e. directly train and evaluate your neural net, there’s no need to train a separate label model anymore as in Snorkel

✅ With a better test set performance and enhanced robustness against correlated or inaccurate LFs than prior methods like Snorkel

🌟 Github: https://github.com/autonlab/weasel

📖 Paper: https://arxiv.org/abs/2107.02233

Have you used any other weak supervision library? Please share in the comments!

#nlp #machinelearning #datascience #researchpaper #github #dataprogramming #weaksupervision