Tempe, AZ, United States of America

Sachin Grover


Average Co-Inventor Count = 2.0

ph-index = 1

Forward Citations = 4(Granted Patents)


Company Filing History:


Years Active: 2020-2021

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2 patents (USPTO):

Title: Sachin Grover: Innovator in Machine Learning Model Compression

Introduction

Sachin Grover is a notable inventor based in Tempe, AZ (US). He has made significant contributions to the field of machine learning, particularly in the area of natural language processing. With a total of 2 patents, Grover's work focuses on enhancing the efficiency of machine learning models.

Latest Patents

Grover's latest patents revolve around the compression of machine learned models. These patents describe devices and techniques for compressing natural language processing models. A first index value to a first address of a weight table is stored in a hash table. This first address stores a first weight associated with a first feature of a natural language processing model. Additionally, a second index value to a second address of the weight table is also stored in the hash table. The second address holds a second weight associated with a second feature of the model. A first code associated with the first feature is generated, comprising a first number of bits. Furthermore, a second code is generated for the second feature, which comprises a second number of bits greater than the first number of bits, based on the magnitude of the second weight being greater than that of the first weight.

Career Highlights

Sachin Grover is currently employed at Amazon Technologies, Inc., where he continues to innovate and develop new technologies. His work has been instrumental in advancing the capabilities of machine learning applications.

Collaborations

One of Grover's notable coworkers is Grant Strimel. Together, they contribute to the innovative projects at Amazon Technologies, Inc.

Conclusion

Sachin Grover is a prominent figure in the field of machine learning, with a focus on model compression techniques. His contributions are paving the way for more efficient natural language processing applications.

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