Uploaded July 2024 | Updated September 2026, 2 weeks ago
Knowledge boosting is a novel technique that allows a large model running remotely to operate on time-delayed input during inference, while boosting small model performance running locally. This technique can benefit real-time applications across various domains such as robotics, self-driving vehicles, and audio and video processing.
Paper: Knowledge boosting during low-latency inference, Interspeech 2024
Project page: https://knowledgeboosting.cs.washington.edu/
Knowledge boosting is a novel technique that allows a large model running remotely to operate on time-delayed input during inference, while boosting small model performance running locally. This technique can benefit real-time applications across various domains such as robotics, self-driving vehicles, and audio and video processing.
Paper: Knowledge boosting during low-latency inference, Interspeech 2024
Project page: https://knowledgeboosting.cs.washington.edu/
![[Audio Descriptions] Faculty In Focus: Nirvan Tyagi](https://i.ytimg.com/vi/v8NzVoue-Ac/mqdefault.jpg "[Audio Descriptions] Faculty In Focus: Nirvan Tyagi
In this episode of the Allen School’s “Faculty in Focus” series, Assistant Professor Nirvan Tyagi discusses his work on cryptography techniques such as zero knowledge proofs, which would enable systems used in banking and other services to provide strong privacy for users while protecting them against bad actors. His research aims to give technology users more control over their data while maintaining efficiency and performance.
For a version without audio descriptions, visit https://youtu.be/PMaUVhjEJlA.")
