- How to use meta data in training phase? https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification 6 comments learnmachinelearning
Linking pages
- GitHub - unitaryai/detoxify: Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at contact@unitary.ai. https://github.com/unitaryai/detoxify 2 comments
- &ampampampampampampampampampampampampampampampampampampampampampampampampampampampampampampampampLTBen Byford>Expectations of Kaggle competitions: ethics and provenance&ampampampampampampampampampampampampampampampampampampampampampampampampampampampampampampampampLT/Ben Byford> https://benbyford.com/articles/expectations-of-kaggle-competitions-ethics-and-provenance/ 0 comments
- Fairness Indicators: Scalable Infrastructure for Fair ML Systems – Google AI Blog https://ai.googleblog.com/2019/12/fairness-indicators-scalable.html 0 comments
- Debuggable Deep Networks: Sparse Linear Models (Part 1) – gradient science https://gradientscience.org/glm_saga/ 0 comments
- GitHub - innat/ML-Resource: A concise resource repository for machine learning https://github.com/innat/ML-Bookmarks 0 comments
- Can AI detect the direction of harm? Building a model for message moderation on social media platforms | Jiayong Li https://lijiayong.github.io/posts/direction_of_harm/ 0 comments
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