Browsing by Subject "Efficient learning"

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    ItemOpen Access
    Bağlam ağaçları ile ardışık doğrusal olmayan bağlanım
    (IEEE, 2014-04) Vanlı, N. Denizcan; Kozat, Süleyman S.
    Bu bildiride, ardışık doğrusal olmayan bağlanım problemi incelenmiş ve bağlam ağaçları kullanarak etkili bir öğrenme algoritması sunulmuştur. Bu amaçla, bağlanım alanı parçalara ayrılmış ve oluşan bölgeler bağlam ağacı ile simgelenmiştir. Her bölgede bağımsız bağlanım algoritmaları kullanılarak bağlam ağacı tarafından gösterilebilen tüm doğrusal olmayan modellerin kestirimleri, hesaplama karmaşıklığı bağlam ağacının düğüm sayısıyla doğrusal olan bu algoritma ile uyarlanır olarak birleştirilmiştir. Önerilen algoritmanın performans limitleri, veriler üzerinde istatistiksel varsayımlarda bulunmaksızın incelenmiştir. Ayrıca, teorik sonuçları izah etmek için sayısal bir örnek sunulmuştur.
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    Efficient learning strategies over distributed networks for big data
    (2017-07) Kılıç, Osman Fatih
    We study the problem of online learning over a distributed network, where agents in the network collaboratively estimate an underlying parameter of interest using noisy observations. For the applicability of such systems, sustaining a communication and computation efficiency while providing a comparable performance plays a crucial role. To this end, in this work, we propose computation and communication wise highly efficient distributed online learning methods that present superior performance compared to the state-of-the-art. In the first part of the thesis, we study distributed centralized estimation schemes, where such approaches require high communication bandwidth and high computational load. We introduce a novel approach based on set-membership filtering to reduce such burdens of the system. In the second part of our work, we study distributed decentralized estimation schemes, where nodes in the network individually and collaboratively estimate a dynamically evolving parameter using noisy observations. We present an optimal decentralized learning algorithm through disclosure of local estimates and prove that optimal estimation in such systems is possible only over certain network topologies. We then derive an iterative algorithm to recursively construct the optimal combination weights and the estimation. Through series of simulations over generated and real-life benchmark data, we demonstrate the superior performance of the proposed methods compared to state-of-the-art distributed learning methods. We show that the introduced algorithms provide improved learning rates and lower steady-state error levels while requiring much less communication and computation load on the system.
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    SiameseFuse: A computationally efficient and a not-so-deep network to fuse visible and infrared images
    (Elsevier BV, 2022-04-22) Özer, S.; Ege, Mert; Özkanoglu, Mehmet Akif
    Recent developments in pattern analysis have motivated many researchers to focus on developing deep learning based solutions in various image processing applications. Fusing multi-modal images has been one such application area where the interest is combining different information coming from different modalities in a more visually meaningful and informative way. For that purpose, it is important to first extract salient features from each modality and then fuse them as efficiently and informatively as possible. Recent literature on fusing multi-modal images reports multiple deep solutions that combine both visible (RGB) and infra-red (IR) images. In this paper, we study the performance of various deep solutions available in the literature while seeking an answer to the question: “Do we really need deeper networks to fuse multi-modal images?” To have an answer for that question, we introduce a novel architecture based on Siamese networks to fuse RGB (visible) images with infrared (IR) images and report the state-of-the-art results. We present an extensive analysis on increasing the layer numbers in the architecture with the above-mentioned question in mind to see if using deeper networks (or adding additional layers) adds significant performance in our proposed solution. We report the state-of-the-art results on visually fusing given visible and IR image pairs in multiple performance metrics, while requiring the least number of trainable parameters. Our experimental results suggest that shallow networks (as in our proposed solutions in this paper) can fuse both visible and IR images as well as the deep networks that were previously proposed in the literature (we were able to reduce the total number of trainable parameters up to 96.5%, compare 2,625 trainable parameters to the 74,193 trainable parameters).