Ali H. Mirza
Bilkent University
AutoencoderAdaptive filterTime domainAlgorithmMachine learningRandom variableLogic gateWired communicationMean squared errorRecurrent neural networkExponential functionLogistic regressionArtificial intelligenceSet (abstract data type)Computational complexity theoryFrequency domainGatingStatistical assumptionOnline algorithmStochastic gradient descentWireless sensor networkEfficient energy usePerformance improvementCritical factorsComputer networkMathematicsIntrusion detection systemComputer scienceMatrix decompositionMultiplicative functionEnsemble learningBoosting (machine learning)ScalabilityArtificial neural networkRegretFeature extractionCombinatorial optimizationWirelessDimensionality reductionAnomaly detectionFast Fourier transformExponential growthOperator (computer programming)Real-time computingUnsupervised learningRegressionDecision treeLinear combinationClassifier (UML)Robustness (computer science)
8Publications
3H-index
37Citations
Publications 8
Newest
#1Tolga Ergen (Stanford University)H-Index: 9
#2Ali H. Mirza (Bilkent University)H-Index: 3
Last. Suleyman S. Kozat (Bilkent University)H-Index: 21
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We investigate variable-length data regression in an online setting and introduce an energy-efficient regression structure build on long short-term memory (LSTM) networks. For this structure, we also introduce highly effective online training algorithms. We first provide a generic LSTM-based regression structure for variable-length input sequences. To reduce the complexity of this structure, we then replace the regular multiplication operations with an energy-efficient operator, i.e., the ef-ope...
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#1Dariush Kari (UIUC: University of Illinois at Urbana–Champaign)H-Index: 2
#2Ali H. Mirza (Bilkent University)H-Index: 3
Last. Suleyman S. Kozat (Bilkent University)H-Index: 21
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We introduce the boosting notion of machine learning to the adaptive signal processing literature. In our framework, we have several adaptive filtering algorithms, i.e., the weak learners, that run in parallel on a common task such as equalization, classification, regression or filtering. We specifically provide theoretical bounds for the performance improvement of our proposed algorithms over the conventional adaptive filtering methods under some widely used statistical assumptions. We demonstr...
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#1Ali H. Mirza (Bilkent University)H-Index: 3
In this paper, we propose a boosted regression algorithm in an online framework. We have a linear combination of the estimated output for each weak learner and weigh each of the estimated output differently by introducing ensemble coefficients. We then update the ensemble weight coefficients using both additive and multiplicative updates along with the stochastic gradient updates of the regression weight coefficients. We make the proposed algorithm robust by introducing two critical factors; sig...
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#1Ali H. Mirza (Bilkent University)H-Index: 3
In this paper, we formulate several variants of the mixture of both the additive and multiplicative updates using stochastic gradient descent (SGD) and exponential gradient (EG) algorithms respectively. We employ these updates on the gated recurrent unit (GRU) networks. We then derive the gradient-based updates for the parameters of the GRU networks. We propose four different updates as a mean, minimum, even-odd and balanced set of updates for the GRU network. Through an extensive set of experim...
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#1Ali H. Mirza (Bilkent University)H-Index: 3
In this paper, we derived the online additive updates of gated recurrent unit (GRU) network by using fast fourier transform-inverse fast fourier transform (FFT-IFFT) operator. In the gating process of the GRU networks, we work in the frequency domain and execute all the linear operations. For the non-linear functions in the gating process, we first shift back to the time domain and then apply non-linear GRU gating functions. Furthermore, in order to reduce the computational complexity and speed ...
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#1Ali H. Mirza (Bilkent University)H-Index: 3
In this paper, we execute anomaly detection over the computer networks using various machine learning algorithms. We then combine these algorithms to boost the overall performance. We implement three different types of classifiers, i.e, neural networks, decision trees and logistic regression. We then boost the overall performance of the intrusion detection algorithm using ensemble learning. In ensemble learning, we employ weighted majority voting scheme based on the individual classifier perform...
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#1Ali H. Mirza (Bilkent University)H-Index: 3
#2Selin Cosan (Bilkent University)H-Index: 1
In this paper, we introduce a sequential autoencoder framework using long short term memory (LSTM) neural network for computer network intrusion detection. We exploit the dimensionality reduction and feature extraction property of the autoencoder framework to efficiently carry out the reconstruction process. Furthermore, we use the LSTM networks to handle the sequential nature of the computer network data. We assign a threshold value based on cross-validation in order to classify whether the inc...
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#1Muhammad Anjum Qureshi (Bilkent University)H-Index: 5
#2Wardah Sarmad (Bilkent University)
Last. Ali H. Mirza (Bilkent University)H-Index: 3
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Wireless communication is considered to be more challenging than the typical wired communication due to unpredictable channel conditions. In this paper, we target coverage area problem, where a group of sensors is selected from a set of sensors placed in a particular area to maximize the coverage provided to that area. The constraints to this optimization are the battery power of the sensor and number of sensors that are active at a given time. We consider a variant of the coverage related to a ...
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