This inventor holds 13 USPTO granted patents and 3 published patent applications and 1 EPO patent. Top assignee: Google Inc.. Active years: 2019-2026.
Location History:
- Jersey City, NJ (US) (2019)
- Elmhurst, NJ (US) (2022)
- Elmhurst, NY (US) (2020 - 2024)
Company Filing History:
Years Active: 2019-2026
Title: Ruiqi Guo: Innovator in Embedding Models and kNN Computation
Introduction
Ruiqi Guo is a prominent inventor based in Elmhurst, NJ (US), known for his contributions to the fields of machine learning and data processing. With a total of 13 patents to his name, Guo has made significant strides in enhancing the efficiency of embedding models and k-nearest neighbor (kNN) computations.
Latest Patents
One of Guo's latest patents is titled "Training embedding models using a stale embedding cache for negative sampling." This innovation provides systems and methods that efficiently train embedding models by utilizing a cache of item embeddings for candidate items over multiple training iterations. The cached item embeddings can be 'stale' embeddings generated by a previous version of the model. At each iteration, these embeddings are used to generate similarity scores, which are crucial for sampling negative items in the current training iteration. This approach allows for the approximation of a true gradient, ultimately improving the model's training process.
Another notable patent is "Approximate k nearest neighbors on hardware accelerators." This patent outlines methods, systems, and apparatus for performing kNN computations using hardware accelerators. The method involves obtaining a set of query vectors and database vectors, then performing a search for the k most similar database vectors for each query vector. The hardware accelerator computes similarity values and identifies the most similar database vectors, enhancing the speed and efficiency of the kNN process.
Career Highlights
Ruiqi Guo is currently employed at Google Inc., where he continues to innovate and contribute to advancements in technology. His work focuses on improving machine learning algorithms and data processing techniques, making significant impacts in the tech industry.
Collaborations
Guo collaborates with notable colleagues, including Sanjiv Kumar and David Morris Simcha, who contribute to his projects and research endeavors.
Conclusion
Ruiqi Guo's innovative work in embedding models and kNN computations showcases his expertise and dedication to advancing technology. His contributions are paving the way for more efficient machine learning applications and data processing methods.