Reimagining service delivery in the agentic era with Google Public Sector
cloud.google.com·19h ago
AI Research
arXiv CS.AI·May 25·5 min read
Sutra: Tensor-Op RNNs as a Compilation Target for Vector Symbolic Architectures
TL;DR
Vector Symbolic Architectures (VSAs) lacked a practical implementation for neural networks. Sutra is a functional programming language that compiles to a PyTorch neural network, enabling tensor operations to represent complex logic.
✦ Why It Matters
Engineers can leverage Sutra to create efficient neural networks that also incorporate symbolic reasoning.
Key Takeaways
- Utilize Sutra for projects needing symbolic reasoning and neural networks.
- Leverage the 100% accuracy achieved across multiple embeddings.
- Explore the integration of logic programming with machine learning.
- Consider the legibility of trained models as code for easier debugging.
How It Works
Sutra compiles programs into a tensor operation graph, allowing various programming constructs to be represented as tensor operations. This includes advanced features like rotation binding and polynomial Kleene logic, which facilitate complex reasoning tasks.
The compiled graph can then be executed within the PyTorch framework, enabling efficient training and inference.