Search Results for author: Nate Gillman

Found 2 papers, 2 papers with code

Self-Correcting Self-Consuming Loops for Generative Model Training

1 code implementation11 Feb 2024 Nate Gillman, Michael Freeman, Daksh Aggarwal, Chia-Hong Hsu, Calvin Luo, Yonglong Tian, Chen Sun

As synthetic data becomes higher quality and proliferates on the internet, machine learning models are increasingly trained on a mix of human- and machine-generated data.

Motion Synthesis Representation Learning

IsoScore: Measuring the Uniformity of Embedding Space Utilization

1 code implementation Findings (ACL) 2022 William Rudman, Nate Gillman, Taylor Rayne, Carsten Eickhoff

We propose IsoScore: a novel tool that quantifies the degree to which a point cloud uniformly utilizes the ambient vector space.

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