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Found 6 papers, 0 papers with code
VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise
The empirical experiments show that the proposed VQUNet provides better robustness to the target DNN models, and it outperforms other state-of-the-art noise-reduction-based defense methods under various adversarial attacks for both Fashion-MNIST and CIFAR10 datasets.
L-SR1 Adaptive Regularization by Cubics for Deep Learning
To avoid these points, directions of negative curvature can be utilized, which requires computing the second-derivative matrix.
Stochastic Induction of Decision Trees with Application to Learning Haar Tree
Experimentally, the algorithm is compared with several other related state-of-the-art decision tree learning methods, including the baseline non-stochastic approach.
Synthetic Reduced Nearest Neighbor Model for Regression
This algorithm is comprised of two steps: (1) The assignment step, where assignments of the samples to each centroid is found and the target response (i. e., prediction) of each centroid is determined; and (2) the update/centroid step, where each centroid is updated such that the loss function of the entire model is minimized.
Adversarial Poisoning Attacks and Defense for General Multi-Class Models Based On Synthetic Reduced Nearest Neighbors
Through extensive experimental analysis, we demonstrate that (i) the proposed attack technique can deteriorate the accuracy of several models drastically, and (ii) under the proposed attack, the proposed defense technique significantly outperforms other conventional machine learning models in recovering the accuracy of the targeted model.
Learning from Richer Human Guidance: Augmenting Comparison-Based Learning with Feature Queries
We focus on learning the desired objective function for a robot.
