AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 2 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This paper presents Maverick, a novel approach to private and verifiable LLM inference based on a protocol for delegating matrix-vector multiplication, a dominant operation in LLMs, and combines this verification primitive with LPN-based pseudorandom masking to provide input privacy.
PANDA is a scalable system that uses zero-knowledge proofs to prove the robustness and fairness properties of a model without revealing its private parameters, and can generate proofs of local robustness for neural networks with more than 2.9M parameters in 5 minutes, and can verify them in 10 seconds.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.