Cambridge, MA, United States of America

Maohao Shen

This inventor holds 1 USPTO granted patent and 2 published patent applications. Top assignees: International Business Machines Corporation, Massachusetts Institute of Technology. Active years: 2026.

USPTO Granted Patents = 1 

Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: The Innovative Mind of Maohao Shen

Introduction

Maohao Shen is a prominent inventor based in Cambridge, MA (US). He has made significant contributions to the field of technology, particularly in the area of prompt tuning for machine learning models. His innovative approach has led to the development of a unique patent that enhances the efficiency of model predictions.

Latest Patents

Maohao Shen holds a patent titled "Reliable gradient-free and likelihood-free prompt tuning." This invention involves drawing prompt embedding samples from a prior distribution and passing them into a pretrained model to receive corresponding token label predictions for a batch of text data. The process includes accepting samples based on a distance function condition, resampling embeddings, and propagating them through the pretrained model. This method allows for improved inferencing by concatenating projected resampled embeddings with input embeddings, ultimately enhancing the model's performance.

Career Highlights

Throughout his career, Maohao Shen has worked with notable organizations such as IBM and the Massachusetts Institute of Technology. His experience in these prestigious institutions has allowed him to collaborate with leading experts in the field and contribute to groundbreaking research and development.

Collaborations

Maohao has collaborated with talented individuals like Soumya Ghosh and Prasanna Sattigeri. These partnerships have fostered an environment of innovation and creativity, leading to advancements in their respective fields.

Conclusion

Maohao Shen's contributions to technology through his patent and collaborations highlight his role as an influential inventor. His work continues to inspire advancements in machine learning and prompt tuning methodologies.

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