Bengaluru, India

Asis Kumar Patra

USPTO Granted Patents = 1 

Average Co-Inventor Count = 3.0

ph-index = 1


Company Filing History:


Years Active: 2022

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1 patent (USPTO):Explore Patents

Title: Asis Kumar Patra: Innovator in Hyperparameter Tuning

Introduction

Asis Kumar Patra is a notable inventor based in Bengaluru, India. He has made significant contributions to the field of deep learning, particularly in the area of hyperparameter tuning. His innovative approach has the potential to enhance the efficiency of large-scale deep learning workloads.

Latest Patents

Asis Kumar Patra holds a patent for a method titled "Resource allocation for tuning hyperparameters of large-scale deep learning workloads." This invention involves a computer system and program product that performs hyperparameter tuning. The process begins with receiving hyperparameter search input data, followed by invoking a search algorithm to generate a set of hyperparameters. A hyperparameter tuning resource allocator receives physical resource data regarding a cluster and determines the required workload. It also assesses whether an option to mandate the workload batch size was selected. The cluster then performs the number of parallel experiments necessary to search and tune the set of hyperparameters, managed according to the physical resource data and the required workload through tensor swapping.

Career Highlights

Asis Kumar Patra is currently employed at International Business Machines Corporation (IBM). His work at IBM focuses on advancing technologies that improve the performance of deep learning systems. His expertise in resource allocation and hyperparameter tuning has positioned him as a key player in the field.

Collaborations

Asis has collaborated with talented coworkers such as Saritha Vinod and Anto Ajay Raj John. Their combined efforts contribute to the innovative projects at IBM, fostering a collaborative environment that drives technological advancements.

Conclusion

Asis Kumar Patra is a distinguished inventor whose work in hyperparameter tuning is paving the way for more efficient deep learning applications. His contributions to the field are invaluable and continue to influence the development of advanced technologies.

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