Google AI Blog: Harnessing Organizational Knowledge for Machine Learning (2019)(About) how existing knowledge in an organization can be used as noisier, higher-level supervision—or, as it is often termed, weak supervision—to quickly label large training datasets
Snorkel Drybell, experimental internal system, which adapts the opensource
Snorkel framework to **use diverse organizational knowledge
resources—like internal models, ontologies, legacy rules, knowledge
graphs and more—in order to generate training data** for machine learning
models at web scale.
Enables writing **labeling functions** that label training data programmatically
Hamiltonian Neural Networks(About) > Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? In this paper, we draw inspiration from Hamiltonian mechanics to train models that learn and respect exact conservation laws in an unsupervised manner.