Dalton James Jones

Title: Dalton James Jones: Innovator in Machine Learning Caching

Introduction

Dalton James Jones is a prominent inventor based in Cupertino, CA. He has made significant contributions to the field of machine learning, particularly in the area of efficient caching techniques. His innovative approach has garnered attention in the tech community.

Latest Patents

Dalton holds a patent titled "Efficient machine learning caching via attention output-based token eviction." This patent outlines techniques and apparatus for machine learning. In his method, an input prompt comprising a set of tokens is accessed as input to a generative machine learning model. A first key tensor and a first value tensor are generated for a first token of the set of tokens, and these tensors are stored in memory. A first retention score is generated for the first token based on the first key tensor, the first value tensor, and a second token of the set of tokens. The first key tensor and the first value tensor are evicted from memory when it is determined that the first retention score is the lowest retention score of the memory. This innovative approach enhances the efficiency of machine learning processes.

Career Highlights

Dalton is currently employed at Qualcomm Incorporated, a leading technology company known for its advancements in telecommunications and computing. His work at Qualcomm has allowed him to explore and develop cutting-edge technologies in machine learning.

Collaborations

Dalton has collaborated with notable colleagues, including Mingu Lee and Matthew Harper Langston. These collaborations have contributed to the development of innovative solutions in their respective fields.

Conclusion

Dalton James Jones is a key figure in the realm of machine learning innovations. His patent on efficient caching techniques demonstrates his commitment to advancing technology. His work at Qualcomm and collaborations with talented individuals further highlight his impact on the industry.