Bayesian Estimators for Classifier Testing from Noisy Labels

Invention type: Technology / Case number: #24963L
License tlo.mit.edu/contact-us

Researchers

Jonathan Su

Departments: Lincoln Laboratory
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML)
Impact Areas: Connected World

  • bayesian estimators and expectation maximization for classifier testing from noisy labels
    Patent Cooperation Treaty | Published application

License this technology

Interested in this technology? Connect with our experienced licensing team to initiate the process.

Contact Us

Sign up for technology updates

Sign up now to receive the latest updates on cutting-edge technologies and innovations.

Sign Up

More Technologies

A Machine Learning-guided Whole-Organ Optical Imaging Technique for Non-destructive Identification of Early Pre-Cancer Lesions and Harvesting of Viable Cells from the same for Organoid Culture

#25480
Angela Belcher / Vivek Rastogi / Neelkanth Manoj Bardhan
Artificial Intelligence (AI) and Machine Learning (ML) / Biotechnology / Chemicals & Materials / Drug Discovery and Research Tools / Electronics & Photonics / Sensing & Imaging

Systolic AI Processor Compiler

#25433L
Bill Song
Artificial Intelligence (AI) and Machine Learning (ML)

Provably Secure Large Language Models

#25397J
Boris Katz / Juan Ignacio Cases Martin / Christian Arnold / Abdul Alabdulkareem / Andrei Barbu
Artificial Intelligence (AI) and Machine Learning (ML) / Computer Science