Publication

MILEAGE: Multiple Instance LEArning with Global Embedding

International Conference on Machine Learning (ICML)


Abstract

Multiple Instance Learning (MIL) generally represents each example as a collection of instances such that the features for local objects can be better captured, whereas traditional methods typically extract a global feature vector for each example as an integral part. However, there is limited research work on investigating which of the two learning scenarios performs better.

This paper proposes a novel framework – Multiple Instance LEArning with Global Embedding (MILEAGE), in which the global feature vectors for traditional learning methods are integrated into the MIL setting. Within the proposed framework, a large margin method is formulated to adaptively tune the weights on the two different kinds of feature representations (i.e., global and multiple instance) for each example and trains the classifier simultaneously.

An extensive set of experiments are conducted to demonstrate the advantages of the proposed method.

Related Publications

All Publications

NeurIPS - December 6, 2021

Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement

Samuel Daulton, Maximilian Balandat, Eytan Bakshy

UAI - July 27, 2021

Measuring Data Leakage in Machine-Learning Models with Fisher Information

Awni Hannun, Chuan Guo, Laurens van der Maaten

arXiv - January 29, 2020

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J. Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzal, Adriana Romero, Michael Rabbat, Pascal Vincent, Nafissa Yakubova, James Pinkerton, Duo Wang, Erich Owens, Larry Zitnick, Michael P. Recht, Daniel K. Sodickson, Yvonne W. Lui

NeurIPS - December 6, 2021

CRYPTEN: Secure Multi-Party Computation Meets Machine Learning

Brian Knott, Shobha Venkataraman, Awni Hannun, Shubho Sengupta, Mark Ibrahim, Laurens van der Maaten

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookie Policy