9 citations · 22 across the 8 of their papers we have counts for
8 papers
Defending against substitute model black box adversarial attacks with the 01 loss
Yunzhe Xue, Meiyan Xie, Usman Roshan
Substitute model black box attacks can create adversarial examples for a target model just by accessing its output labels. This poses a major challenge to machine learning models i…
Towards adversarial robustness with 01 loss neural networks
Yunzhe Xue, Meiyan Xie, Usman Roshan
Motivated by the general robustness properties of the 01 loss we propose a single hidden layer 01 loss neural network trained with stochastic coordinate descent as a defense agains…
On the transferability of adversarial examples between convex and 01 loss models
Yunzhe Xue, Meiyan Xie, Usman Roshan
The 01 loss gives different and more accurate boundaries than convex loss models in the presence of outliers. Could the difference of boundaries translate to adversarial examples t…
Robust binary classification with the 01 loss
Yunzhe Xue, Meiyan Xie, Usman Roshan
The 01 loss is robust to outliers and tolerant to noisy data compared to convex loss functions. We conjecture that the 01 loss may also be more robust to adversarial attacks. To st…
A fully 3D multi-path convolutional neural network with feature fusion and feature weighting for automatic lesion identification in brain MRI images
Yunzhe Xue, Meiyan Xie, Fadi G. Farhat +5
We propose a fully 3D multi-path convolutional network to predict stroke lesions from 3D brain MRI images. Our multi-path model has independent encoders for different modalities co…
A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images
Yunzhe Xue, Fadi G. Farhat, Olga Boukrina +4
Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning. We prop…
Image classification and retrieval with random depthwise signed convolutional neural networks
Yunzhe Xue, Usman Roshan
We propose a random convolutional neural network to generate a feature space in which we study image classification and retrieval performance. Put briefly we apply random convoluti…
MaxSSmap: A GPU program for mapping divergent short reads to genomes with the maximum scoring subsequence
Turki Turki, Usman Roshan
Programs based on hash tables and Burrows-Wheeler are very fast for mapping short reads to genomes but have low accuracy in the presence of mismatches and gaps. Such reads can be a…