18 citations · 18 across the 1 of their papers we have counts for
3 papers
Feasible Learning
Juan Ramirez, Ignacio Hounie, Juan Elenter +4
We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bounds the loss for each training sample. In…
Beyond Local Nash Equilibria for Adversarial Networks
Frans A. Oliehoek, Rahul Savani, Jose Gallego +2
Save for some special cases, current training methods for Generative Adversarial Networks (GANs) are at best guaranteed to converge to a `local Nash equilibrium` (LNE). Such LNEs,…
GANGs: Generative Adversarial Network Games
Frans A. Oliehoek, Rahul Savani, Jose Gallego-Posada +3
Generative Adversarial Networks (GAN) have become one of the most successful frameworks for unsupervised generative modeling. As GANs are difficult to train much research has focus…