Jersey City, NJ, United States of America

Mark M Sandler

USPTO Granted Patents = 9 

Average Co-Inventor Count = 3.1

ph-index = 3

Forward Citations = 316(Granted Patents)


Location History:

  • Jersey City, NJ (US) (2012 - 2016)
  • Mountain View, CA (US) (2014 - 2023)

Company Filing History:


Years Active: 2012-2024

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9 patents (USPTO):Explore Patents

Title: Mark M Sandler: Innovator in Machine Learning and Neural Networks

Introduction

Mark M Sandler is a prominent inventor based in Jersey City, NJ (US). He has made significant contributions to the fields of machine learning and neural networks, holding a total of 9 patents. His work focuses on enhancing the efficiency and effectiveness of machine learning models.

Latest Patents

One of his latest patents is titled "Parameter-efficient multi-task and transfer learning." This patent provides systems and methods that enable parameter-efficient transfer learning, multi-task learning, and model repurposing. The invention allows a computing system to modify a previously trained machine-learned model to perform a different task while keeping some parameters fixed.

Another notable patent is "Highly efficient convolutional neural networks." This patent introduces new neural network architectures that include linear bottleneck layers and inverted residual blocks. These innovations aim to improve the efficiency of neural networks, making them more effective for various applications.

Career Highlights

Mark M Sandler is currently employed at Google Inc., where he continues to push the boundaries of technology through his innovative work. His contributions have been instrumental in advancing the capabilities of machine learning and artificial intelligence.

Collaborations

Throughout his career, Mark has collaborated with notable coworkers such as Andrew Gerald Howard and Andrey Zhmoginov. These collaborations have further enriched his research and development efforts in the field.

Conclusion

Mark M Sandler is a key figure in the innovation of machine learning and neural networks. His patents reflect a commitment to enhancing technology and improving the efficiency of computational models.

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