Cambridge, MA, United States of America

Phillip John Isola


Average Co-Inventor Count = 3.3

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2016-2022

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2 patents (USPTO):Explore Patents

Title: Innovations of Phillip John Isola

Introduction

Phillip John Isola is a prominent inventor based in Cambridge, MA. He has made significant contributions to the field of machine learning and image understanding. With a total of 2 patents, his work focuses on enhancing supervised learning methodologies and image representation.

Latest Patents

Isola's latest patents include "Supervised contrastive learning with multiple positive examples." This patent presents an improved training methodology that enables supervised contrastive learning to be performed across multiple positive and negative training examples. The techniques adapt contrastive learning to the fully supervised setting, allowing learning to occur simultaneously across multiple positive examples. Another notable patent is "Layered image understanding," which is directed towards generating a layered scene representation for an image. This representation explains the scene by defining its semantic structure, recognizing objects within the image, and modeling them into semantic segments.

Career Highlights

Throughout his career, Isola has worked with notable companies such as Microsoft Technology Licensing, LLC and Google Inc. His experience in these leading technology firms has contributed to his expertise in the field of artificial intelligence and image processing.

Collaborations

Isola has collaborated with esteemed colleagues, including Ce Liu and Dilip Krishnan. Their joint efforts have further advanced the research and development of innovative technologies in machine learning.

Conclusion

Phillip John Isola's contributions to supervised learning and image understanding have positioned him as a key figure in the field of artificial intelligence. His patents reflect a commitment to advancing technology and improving methodologies in machine learning.

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