Browsing by Author "Ergen, Tolga"

Now showing items 1-11 of 11

    • An efficient bandit algorithm for general weight assignments 

      Gökçesu, Kaan; Ergen, Tolga; Çiftçi, S.; Kozat, Süleyman Serdar (IEEE, 2017)
      In this paper, we study the adversarial multi armed bandit problem and present a generally implementable efficient bandit arm selection structure. Since we do not have any statistical assumptions on the bandit arm losses, ...

    • Efficient online learning algorithms based on LSTM neural networks 

      Ergen, Tolga; Kozat, Süleyman Serdar (Institute of Electrical and Electronics Engineers, 2018)
      We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online ...

    • A highly efficient recurrent neural network architecture for data regression 

      Ergen, Tolga; Ceyani, Emir (IEEE, 2018)
      In this paper, we study online nonlinear data regression and propose a highly efficient long short term memory (LSTM) network based architecture. Here, we also introduce on-line training algorithms to learn the parameters ...

    • Neural networks based online learning 

      Ergen, Tolga; Kozat, Süleyman Serdar (IEEE, 2017)
      In this paper, we investigate online nonlinear regression and introduce novel algorithms based on the long short term memory (LSTM) networks. We first put the underlying architecture in a nonlinear state space form and ...

    • A novel anomaly detection approach based on neural networks 

      Ergen, Tolga; Kerpiççi, Mine (Institute of Electrical and Electronics Engineers, 2018)
      In this paper, we introduce a Long Short Term Memory (LSTM) networks based anomaly detection algorithm, which works in an unsupervised framework. We first introduce LSTM based structure for variable length data sequences ...

    • A novel distributed anomaly detection algorithm based on support vector machines 

      Ergen, Tolga; Kozat, Süleyman S. (Elsevier, 2020-01)
      In this paper, we study anomaly detection in a distributed network of nodes and introduce a novel algorithm based on Support Vector Machines (SVMs). We first reformulate the conventional SVM optimization problem for a ...

    • Novelty detection using soft partitioning and hierarchical models 

      Ergen, Tolga; Gökçesu, Kaan; Şimşek, Mustafa; Kozat, Süleyman Serdar (IEEE, 2017)
      In this paper, we study novelty detection problem and introduce an online algorithm. The algorithm sequentially receives an observation, generates a decision and then updates its parameters. In the first step, to model the ...

    • Online distributed nonlinear regression via neural networks 

      Ergen, Tolga; Kozat, Süleyman Serdar (IEEE, 2017)
      In this paper, we study the nonlinear regression problem in a network of nodes and introduce long short term memory (LSTM) based algorithms. In order to learn the parameters of the LSTM architecture in an online manner, ...

    • Online learning with recurrent neural networks 

      Ergen, Tolga (Bilkent University, 2018-07)
      In this thesis, we study online learning with Recurrent Neural Networks (RNNs). Particularly, in Chapter 2, we investigate online nonlinear regression and introduce novel regression structures based on the Long Short ...

    • Recurrent neural networks based online learning algorithms for distributed systems 

      Ergen, Tolga; Şahin, S. Onur; Kozat, S. Serdar (Institute of Electrical and Electronics Engineers, 2018)
      In this paper, we investigate online parameter learning for Long Short Term Memory (LSTM) architectures in distributed networks. Here, we first introduce an LSTM based structure for regression. Then, we provide the equations ...

    • Team-optimal online estimation of dynamic parameters over distributed tree networks 

      Kılıç, O. F.; Ergen, Tolga; Sayın, M.; Kozat, Süleyman (Elsevier, 2019)
      We study online parameter estimation over a distributed network, where the nodes in the network collaboratively estimate a dynamically evolving parameter using noisy observations. The nodes in the network are equipped with ...