EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Aug 14·5 min read
An Anecdote Against Slop Artifacts
TL;DR
A new method for verifying exact samplers of continuous distributions using discrete program logic was developed, showcasing elegant proofs and implementations. The work culminated in verified sampling algorithms that challenge conventional understanding.
✦ Why It Matters
Consider exploring program logic techniques to enhance the reliability of your sampling algorithms.
Key Takeaways
- Explore program logic for verifying probabilistic algorithms.
- Utilize the Iris framework for formal verification tasks.
- Understand dyadic rational numbers for better algorithm implementation.
- Consider the adequacy theorem in your probabilistic programming.
How It Works
The core idea involves using program logic to prove properties about real number representations in a discrete programming context. The adequacy theorem connects the program's probabilistic output to dyadic rational comparisons, allowing for rigorous verification of sampling algorithms.