Search Results for author:
Found 7 papers, 6 papers with code
Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach
Adversarial training (AT) is a canonical method for enhancing the robustness of deep neural networks (DNNs).
Robust Unlearnable Examples: Protecting Data Against Adversarial Learning
To address this concern, methods are proposed to make data unlearnable for deep learning models by adding a type of error-minimizing noise.
Knowledge Removal in Sampling-based Bayesian Inference
In this paper, we propose the first machine unlearning algorithm for MCMC.
Robust Unlearnable Examples: Protecting Data Privacy Against Adversarial Learning
To address this concern, methods are proposed to make data unlearnable for deep learning models by adding a type of error-minimizing noise.
Bayesian Inference Forgetting
This paper proposes a {\it Bayesian inference forgetting} (BIF) framework to realize the right to be forgotten in Bayesian inference.
Robustness, Privacy, and Generalization of Adversarial Training
This measure can be approximate empirically by an asymptotically consistent empirical estimator, {\it empirical robustified intensity}.
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting
Thus it motivates us to design a similar mechanism named {\it artificial neural variability} (ANV), which helps artificial neural networks learn some advantages from ``natural'' neural networks.
