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Conference Object Yazilim Mühendisligi Egitiminde Bitirme Projesinin Yürütülmesinde İki Farkli Yöntemin Ögrenci Bakis Açisiyla Degerlendirilmesi(CEUR-WS, 2015) Karakaya,M.; Bostan,A.[No abstract available]Conference Object Deep Learning and Current Trends in Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2018) Bostan,A.; Ekin,C.; Sengul,G.; Karakaya,M.; Tirkes,G.Academic interest and commercial attention can be used to identify how much potential a novel technology may have. Since the prospective advantages in it may help solving some problems that are not solved yet or improving the performance of readily available ones. In this study, we have investigated the Web of Science (WOS) indexing service database for the publications on Deep Learning (DL), Machine Learning (ML), Convolutional Neural Networks (CNN), and Image Processing to reveal out the current trend. The figures indicate the strong potential in DL approach especially in image processing domain. © 2018 IEEE.Article Citation - WoS: 1Citation - Scopus: 2Ss-Mla: a Semisupervised Method for Multi-Label Annotation of Remotely Sensed Images(SPIE, 2021) Üstünkök,T.; Karakaya,M.Recent technological advancements in satellite imagery have increased the production of remotely sensed images. Therefore, developing efficient methods for annotating these images has gained popularity. Most of the current state-of-the-art methods are based on supervised machine learning techniques. We propose a method called semisupervised multi-label annotizer (SS-MLA) that adapts vector-quantized temporal associative memory to annotate remotely sensed images. One of the advantages of SS-MLA over the supervised methods is that it extracts features not only from the given sample but also from similar samples that are previously seen without using an explicit attention mechanism. Thus SS-MLA enhances the learning efficiency of the training process. We conduct extensive performance comparisons with five different methods in the literature over four datasets. The comparison results indicate the success of the proposed method over the existing ones: SS-MLA generates the best results in 7 out of 11 comparisons. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).Conference Object Citation - Scopus: 5Topic-Controlled Text Generation(Institute of Electrical and Electronics Engineers Inc., 2021) Çağlayan,C.; Karakaya,M.Today, the text generation subject in the field of Natural Language Processing (NLP) has gained a lot of importance. In particular, the quality of the text generated with the emergence of new transformer-based models has reached high levels. In this way, controllable text generation has become an important research area. There are various methods applied for controllable text generation, but since these methods are mostly applied on Recurrent Neural Network (RNN) based encoder decoder models, which were used frequently, studies using transformer-based models are few. Transformer-based models are very successful in long sequences thanks to their parallel working ability. This study aimed to generate Turkish reviews on the desired topics by using a transformer-based language model. We used the method of adding the topic information to the sequential input. We concatenated input token embedding and topic embedding (control) at each time step during the training. As a result, we were able to create Turkish reviews on the specified topics. © 2021 IEEEConference Object Yazilim Mühendisliʇi Eʇitiminde Bitirme Projesi Dersinin Öʇrenci Bakiş Açisiyla Deʇerlendirilmesi(CEUR-WS, 2013) Karakaya,M.; Bostan,A.[No abstract available]
