most citedExtreme Volatility Prediction in Stock Market: When GameStop meets Long Short-Term Memory Networks

2 citations · 5 across the 6 of their papers we have counts for

collaborators

11 papers

cs.LG20211 cited

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…

q-bio.BM2021

Functional Protein Structure Annotation Using a Deep Convolutional Generative Adversarial Network

Ethan Moyer, Jeff Winchell, Isamu Isozaki +3

Identifying novel functional protein structures is at the heart of molecular engineering and molecular biology, requiring an often computationally exhaustive search. We introduce t…

cs.LG20212 cited

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…

cs.CV2021

Robust SleepNets

Yigit Alparslan, Edward Kim

State-of-the-art convolutional neural networks excel in machine learning tasks such as face recognition, and object classification but suffer significantly when adversarial attacks…

cs.LG20211 cited

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…

cs.LG20211 cited

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…