Search Results for author:
Found 10 papers, 1 papers with code
Provable Probabilistic Imaging using Score-Based Generative Priors
We also establish a theoretical analysis for characterizing the convergence of the PMC algorithms.
Demystifying Oversmoothing in Attention-Based Graph Neural Networks
Oversmoothing in Graph Neural Networks (GNNs) refers to the phenomenon where increasing network depth leads to homogeneous node representations.
AdvFunMatch: When Consistent Teaching Meets Adversarial Robustness
To tackle this problem, we propose a simple but effective strategy called Adversarial Function Matching (AdvFunMatch), which aims to match distributions for all data points within the $\ell_p$-norm ball of the training data, in accordance with consistent teaching.
Learning Task-Specific Strategies for Accelerated MRI
Traditional CS-MRI methods often separately address measurement subsampling, image reconstruction, and task prediction, resulting in a suboptimal end-to-end performance.
Lower Difficulty and Better Robustness: A Bregman Divergence Perspective for Adversarial Training
In addition, we discuss the function of entropy in TRADES, and we find that models with high entropy can be better robustness learners.
Alleviating Robust Overfitting of Adversarial Training With Consistency Regularization
Adversarial training (AT) has proven to be one of the most effective ways to defend Deep Neural Networks (DNNs) against adversarial attacks.
Understanding the robustness-accuracy tradeoff by rethinking robust fairness
Then, we explored the effect of another classic smoothing regularizer, namely, the maximum entropy (ME), and we have found ME can also help reduce both inter-class similarity and intra-class variance.
End-to-End Sequential Sampling and Reconstruction for MRI
In this paper, we leverage the sequential nature of MRI measurements, and propose a fully differentiable framework that jointly learns a sequential sampling policy simultaneously with a reconstruction strategy.
Scalable Plug-and-Play ADMM with Convergence Guarantees
Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers.
Online Regularization by Denoising with Applications to Phase Retrieval
Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems.
