Search Results for author: Bülent Yener

Found 11 papers, 0 papers with code

Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition

no code implementations ICML 2020 Kareem Aggour, Bülent Yener

This work considers the canonical polyadic decomposition (CPD) of tensors using proximally regularized sketched alternating least squares algorithms.

Output Randomization: A Novel Defense for both White-box and Black-box Adversarial Models

no code implementations8 Jul 2021 Daniel Park, Haidar Khan, Azer Khan, Alex Gittens, Bülent Yener

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" setting and to the opposite in a "black box" setting.

Patient-Specific Seizure Prediction Using Single Seizure Electroencephalography Recording

no code implementations14 Nov 2020 Zaid Bin Tariq, Arun Iyengar, Lara Marcuse, Hui Su, Bülent Yener

But these models require a considerable number of patient-specific seizures to be recorded for extracting the preictal and interictal EEG data for training a classifier.

EEG Seizure prediction

A survey on practical adversarial examples for malware classifiers

no code implementations6 Nov 2020 Daniel Park, Bülent Yener

To fully understand the impact of adversarial examples on malware detection, we review practical attacks against malware classifiers that generate executable adversarial malware examples.

Malware Detection

Towards Obfuscated Malware Detection for Low Powered IoT Devices

no code implementations6 Nov 2020 Daniel Park, Hannah Powers, Benji Prashker, Leland Liu, Bülent Yener

It is imperative to protect these devices as they become more prevalent in commercial and personal networks.

Malware Detection

Image-driven discriminative and generative machine learning algorithms for establishing microstructure-processing relationships

no code implementations27 Jul 2020 Wufei Ma, Elizabeth Kautz, Arun Baskaran, Aritra Chowdhury, Vineet Joshi, Bülent Yener, Daniel Lewis

A binary alloy (uranium-molybdenum) that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions.

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

no code implementations13 Jun 2019 Elizabeth Kautz, Wufei Ma, Saumyadeep Jana, Arun Devaraj, Vineet Joshi, Bülent Yener, Daniel Lewis

Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system.

Classification General Classification

Thwarting finite difference adversarial attacks with output randomization

no code implementations ICLR 2020 Haidar Khan, Daniel Park, Azer Khan, Bülent Yener

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 opposite "black box" setting.

Adversarial Attack

Deep density ratio estimation for change point detection

no code implementations23 May 2019 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.

Change Point Detection Density Ratio Estimation +1

Generation & Evaluation of Adversarial Examples for Malware Obfuscation

no code implementations9 Apr 2019 Daniel Park, Haidar Khan, Bülent Yener

There has been an increased interest in the application of convolutional neural networks for image based malware classification, but the susceptibility of neural networks to adversarial examples allows malicious actors to evade classifiers.

General Classification Malware Classification

Focal onset seizure prediction using convolutional networks

no code implementations29 May 2018 Haidar Khan, Lara Marcuse, Madeline Fields, Kalina Swann, Bülent Yener

Significance: We demonstrate that a robust set of features can be learned from scalp EEG that characterize the preictal state of focal seizures.

EEG Seizure prediction

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