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Found 7 papers, 1 papers with code
Approximating Probability Distributions by using Wasserstein Generative Adversarial Networks
Studied here are Wasserstein generative adversarial networks (WGANs) with GroupSort neural networks as their discriminators.
Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
In this paper, we study a physics-informed algorithm for Wasserstein Generative Adversarial Networks (WGANs) for uncertainty quantification in solutions of partial differential equations.
HessianFR: An Efficient Hessian-based Follow-the-Ridge Algorithm for Minimax Optimization
Wide applications of differentiable two-player sequential games (e. g., image generation by GANs) have raised much interest and attention of researchers to study efficient and fast algorithms.
Approximate Secular Equations for the Cubic Regularization Subproblem
In this paper, we propose and analyze a novel CRS solver based on an approximate secular equation, which requires only some of the Hessian eigenvalues and is therefore much more efficient.
A Momentum Accelerated Adaptive Cubic Regularization Method for Nonconvex Optimization
The cubic regularization method (CR) and its adaptive version (ARC) are popular Newton-type methods in solving unconstrained non-convex optimization problems, due to its global convergence to local minima under mild conditions.
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
Physics-informed neural networks (PINNs) have attracted significant attention for solving partial differential equations (PDEs) in recent years because they alleviate the curse of dimensionality that appears in traditional methods.
On the Expressive Power of a Variant of the Looped Transformer
Previous works try to explain this from the expressive power and capability perspectives that standard transformers are capable of performing some algorithms.
