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Found 10 papers, 1 papers with code
Invisible Watermarking for Audio Generation Diffusion Models
Diffusion models have gained prominence in the image domain for their capabilities in data generation and transformation, achieving state-of-the-art performance in various tasks in both image and audio domains.
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations
We present in this work a PDE Informed Kriging model (PIK), which introduces PDE information via a set of PDE points and conducts posterior prediction similar to the standard kriging method.
On Computation and Generalization of Generative Adversarial Networks under Spectrum Control
Generative Adversarial Networks (GANs), though powerful, is hard to train.
Learning to Defense by Learning to Attack
From the perspective of generative learning, our proposed method can be viewed as learning a deep generative model for generating adversarial samples, which is adaptive to the robust classification.
On Computation and Generalization of GANs with Spectrum Control
Specifically, we propose a new reparameterization approach for the weight matrices of the discriminator in GANs, which allows us to directly manipulate the spectra of the weight matrices through various regularizers and constraints, without intensively computing singular value decompositions.
Learning to Defend by Learning to Attack
Adversarial training provides a principled approach for training robust neural networks.
On Landscape of Lagrangian Functions and Stochastic Search for Constrained Nonconvex Optimization
However, due to the lack of convexity, their landscape is not well understood and how to find the stable equilibria of the Lagrangian function is still unknown.
A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization
Our theoretical discovery partially corroborates the empirical success of MSGD in training deep neural networks.
Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability
Multiview representation learning is popular for latent factor analysis.
Dropping Convexity for More Efficient and Scalable Online Multiview Learning
Multiview representation learning is very popular for latent factor analysis.
