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Found 2 papers, 2 papers with code
Decentralized Hyper-Gradient Computation over Time-Varying Directed Networks
As a result, the hyper-gradient estimator derived from our optimality condition enjoys two desirable properties; (i) it only requires Push-Sum communication of vectors and (ii) it can operate over time-varying directed networks.
Influence Estimation for Generative Adversarial Networks
To this end, (1) we propose an influence estimation method that uses the Jacobian of the gradient of the generator's loss with respect to the discriminator's parameters (and vice versa) to trace how the absence of an instance in the discriminator's training affects the generator's parameters, and (2) we propose a novel evaluation scheme, in which we assess harmfulness of each training instance on the basis of how GAN evaluation metric (e. g., inception score) is expect to change due to the removal of the instance.
