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Found 3 papers, 1 papers with code
Accelerated Neural Network Training with Rooted Logistic Objectives
Furthermore, we illustrate applications of our novel rooted loss function in generative modeling based downstream applications, such as finetuning StyleGAN model with the rooted loss.
Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization
Therefore, to scale computation and data, these models are inevitably trained in a distributed manner in clusters of nodes, and their updates are aggregated before being applied to the model.
Using Intermediate Forward Iterates for Intermediate Generator Optimization
In score-based models, a generative task is formulated using a parametric model (such as a neural network) to directly learn the gradient of such high dimensional distributions, instead of the density functions themselves, as is done traditionally.
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