Výsledky vyhledávání - "Ozdemir, Onur"

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A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans

Autor: Ozdemir, Onur, Russell, Rebecca L., Berlin, Andrew A.

We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments. Our system is based entirely on 3D convolutional neural networks and achieves state-of

Externí odkaz: http://arxiv.org/abs/1902.03233

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Automated Vulnerability Detection in Source Code Using Deep Representation Learning

Autor: Russell, Rebecca L., Kim, Louis, Hamilton, Lei H., Lazovich, Tomo, Harer, Jacob A., Ozdemir, Onur, Ellingwood, Paul M., McConley, Marc W.

Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code. These vulnerabilities can pose serious risk of exploit and result in system compromise, informat

Externí odkaz: http://arxiv.org/abs/1807.04320

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Learning to Repair Software Vulnerabilities with Generative Adversarial Networks

Autor: Harer, Jacob, Ozdemir, Onur, Lazovich, Tomo, Reale, Christopher P., Russell, Rebecca L., Kim, Louis Y., Chin, Peter

Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target domain without requiring paired labeled examples or source and target do

Externí odkaz: http://arxiv.org/abs/1805.07475

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The Role-Relevance Model for Enhanced Semantic Targeting in Unstructured Text

Autor: George, Christopher A., Ozdemir, Onur, Fournelle, Connie, Moore, Kendra E.

Personalized search provides a potentially powerful tool, however, it is limited due to the large number of roles that a person has: parent, employee, consumer, etc. We present the role-relevance algorithm: a search technique that favors search resul

Externí odkaz: http://arxiv.org/abs/1804.07447

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Automated software vulnerability detection with machine learning

Autor: Harer, Jacob A., Kim, Louis Y., Russell, Rebecca L., Ozdemir, Onur, Kosta, Leonard R., Rangamani, Akshay, Hamilton, Lei H., Centeno, Gabriel I., Key, Jonathan R., Ellingwood, Paul M., Antelman, Erik, Mackay, Alan, McConley, Marc W., Opper, Jeffrey M., Chin, Peter, Lazovich, Tomo

Thousands of security vulnerabilities are discovered in production software each year, either reported publicly to the Common Vulnerabilities and Exposures database or discovered internally in proprietary code. Vulnerabilities often manifest themselv

Externí odkaz: http://arxiv.org/abs/1803.04497

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Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection

Autor: Ozdemir, Onur, Woodward, Benjamin, Berlin, Andrew A.

Motivated by the problem of computer-aided detection (CAD) of pulmonary nodules, we introduce methods to propagate and fuse uncertainty information in a multi-stage Bayesian convolutional neural network (CNN) architecture. The question we seek to ans

Externí odkaz: http://arxiv.org/abs/1712.00497

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