11 citations · 22 across the 8 of their papers we have counts for
5 papers · 1 filter
Output Randomization: A Novel Defense for both White-box and Black-box Adversarial Models
Daniel Park, Haidar Khan, Azer Khan +2
Adversarial examples pose a threat to deep neural network models in a variety of scenarios, from settings where the adversary has complete knowledge of the model in a "white box" s…
Optimal Mini-Batch Size Selection for Fast Gradient Descent
Michael P. Perrone, Haidar Khan, Changhoan Kim +3
This paper presents a methodology for selecting the mini-batch size that minimizes Stochastic Gradient Descent (SGD) learning time for single and multiple learner problems. By deco…
Deep density ratio estimation for change point detection
Haidar Khan, Lara Marcuse, Bülent Yener
In this work, we propose new objective functions to train deep neural network based density ratio estimators and apply it to a change point detection problem. Existing methods use…
Thwarting finite difference adversarial attacks with output randomization
Haidar Khan, Daniel Park, Azer Khan +1
Adversarial examples pose a threat to deep neural network models in a variety of scenarios, from settings where the adversary has complete knowledge of the model and to the opposit…
Focal onset seizure prediction using convolutional networks
Haidar Khan, Lara Marcuse, Madeline Fields +2
Objective: This work investigates the hypothesis that focal seizures can be predicted using scalp electroencephalogram (EEG) data. Our first aim is to learn features that distingui…