Search Results for author:
Found 6 papers, 3 papers with code
Benchmarking the Fairness of Image Upsampling Methods
Recent years have witnessed a rapid development of deep generative models for creating synthetic media, such as images and videos.
Set-Membership Inference Attacks using Data Watermarking
In this work, we propose a set-membership inference attack for generative models using deep image watermarking techniques.
Single-Model Attribution of Generative Models Through Final-Layer Inversion
Recent breakthroughs in generative modeling have sparked interest in practical single-model attribution.
Marginal Tail-Adaptive Normalizing Flows
Learning the tail behavior of a distribution is a notoriously difficult problem.
Copula-Based Normalizing Flows
Normalizing flows, which learn a distribution by transforming the data to samples from a Gaussian base distribution, have proven powerful density approximations.
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery
Many Machine Learning algorithms are formulated as regularized optimization problems, but their performance hinges on a regularization parameter that needs to be calibrated to each application at hand.
