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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Birkbeck, Neil Aylon Charles
- 2Cai, Zhipeng
- 2Chen, Jiyang
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 74Machine Learning
- 70Reinforcement Learning
- 41Artificial Intelligence
- 36Machine learning
- 22Natural Language Processing
- 22Reinforcement learning
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Static detection and identification of X86 malicious executables: A multidisciplinary approach
DownloadFall 2009
In this thesis, we propose a novel approach to detect malicious executables in the network layer using a combination of techniques from bioinformatics, data mining and information retrieval. This approach requires translating malicious code into genome-like representations. Based on their...
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Spring 2019
Software Product Line Engineering (SPLE) creates configurable platforms that can be used to efficiently produce similar, and yet different, product variants. To implement SPLs, multiple variability implementation mechanisms have been suggested, including polymorphism. In this thesis, we talk...
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Spring 2012
We study linear estimation based on perturbed data when performance is measured by a matrix norm of the expected residual error, in particular, the case in which there are many unknowns, but the “best” estimator is sparse, or has small L1-norm. We propose a Lasso-like procedure that finds the...
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Statistically Significant Dependencies for Spatial Co-location Pattern Mining and Classification Association Rule Discovery
DownloadFall 2014
Spatial co-location pattern mining and classification association rule discovery are two canonical tasks studied in the data mining community. Both of them focus on the detection of sets of features that show associations. The difference is that in spatial co-location pattern mining, the features...
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Spring 2014
Spatial interaction pattern mining is the process of discovering patterns that occur due to the interaction of Boolean features from a spatial domain. A positive interaction of a subset of features generates a co-location pattern, whereas a negative interaction of a subset of features generates a...
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Strange springs in many dimensions: how parametric resonance can explain divergence under covariate shift.
DownloadFall 2021
Most convergence guarantees for stochastic gradient descent with momentum (SGDm) rely on independently and identically ditributed (iid) data sampling. Yet, SGDm is often used outside this regime, in settings with temporally correlated inputs such as continual learning and reinforcement learning....
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Fall 2017
Many applications that use geographical databases (a.k.a. gazetteers) rely on the accuracy of the information in the database. However, poor data quality is an issue in gazetteers; often data is integrated from multiple sources with different quality constraints and there may not be much detail...