12 citations · 46 across the 11 of their papers we have counts for
6 papers · 1 filter
ATRAS: Adversarially Trained Robust Architecture Search
Yigit Alparslan, Edward Kim
In this paper, we explore the effect of architecture completeness on adversarial robustness. We train models with different architectures on CIFAR-10 and MNIST dataset. For each mo…
Extreme Volatility Prediction in Stock Market: When GameStop meets Long Short-Term Memory Networks
Yigit Alparslan, Edward Kim
The beginning of 2021 saw a surge in volatility for certain stocks such as GameStop company stock (Ticker GME under NYSE). GameStop stock increased around 10 fold from its decade-l…
Evaluating Online and Offline Accuracy Traversal Algorithms for k-Complete Neural Network Architectures
Yigit Alparslan, Ethan Jacob Moyer, Edward Kim
Architecture sizes for neural networks have been studied widely and several search methods have been offered to find the best architecture size in the shortest amount of time possi…
Towards Searching Efficient and Accurate Neural Network Architectures in Binary Classification Problems
Yigit Alparslan, Ethan Jacob Moyer, Isamu Mclean Isozaki +4
In recent years, deep neural networks have had great success in machine learning and pattern recognition. Architecture size for a neural network contributes significantly to the su…
Adversarial Attacks against Neural Networks in Audio Domain: Exploiting Principal Components
Ken Alparslan, Yigit Alparslan, Matthew Burlick
Adversarial attacks are inputs that are similar to original inputs but altered on purpose. Speech-to-text neural networks that are widely used today are prone to misclassify advers…
Adversarial Attacks on Convolutional Neural Networks in Facial Recognition Domain
Yigit Alparslan, Ken Alparslan, Jeremy Keim-Shenk +2
Numerous recent studies have demonstrated how Deep Neural Network (DNN) classifiers can be fooled by adversarial examples, in which an attacker adds perturbations to an original sa…