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    Histopathological classification of colon tissue images with self-supervised models
    (IEEE, 2023) Department of Computer Engineering; Erden, Mehmet Bahadır; Cansız, Selahattin; Demir, Çiğdem Gündüz; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering
    Deep learning techniques have demonstrated their ability to facilitate medical image diagnostics by offering more precise and accurate predictions. Convolutional neural network (CNN) architectures have been employed for a decade as the primary approach to enable automated diagnosis. On the other hand, recently proposed vision transformers (ViTs) based architectures have shown success in various computer vision tasks. However, their efficacy in medical image classification tasks remains largely unexplored due to their requirement for large datasets. Nevertheless, significant performance gains can be achieved by leveraging self-supervised learning techniques through pretraining. This paper analyzes performance of self-supervised pretrained networks in medical image classification tasks. Results on colon histopathology images revealed that CNN based architectures are more effective when trained from scratch, while pretrained models could achieve similar levels of performance with limited data.
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    Implications of node selection in decentralized federated learning
    (IEEE, 2023) Department of Computer Engineering; Lodhi, Ahnaf Hannan; Akgün, Barış; Özkasap, Öznur; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering
    Decentralized Federated Learning (DFL) offers a fully distributed alternative to Federated Learning (FL). However, the lack of global information in a highly heterogeneous environment negatively impacts its performance. Node selection in FL has been suggested to improve both communication efficiency and convergence rate. In order to assess its impact on DFL performance, this work evaluates node selection using performance metrics. It also proposes and evaluates a time-varying parameterized node selection method for DFL employing validation accuracy and its per-round change. The mentioned criteria are evaluated using both hard and stochastic/soft selection on sparse networks. The results indicate that the bias associated with node selection adversely impacts performance as training progresses. Furthermore, under extreme conditions, soft selection is observed to result in higher diversity for better generalization, while a completely random selection is shown to be preferable with very limited participation.
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    Learning grammatical categories using paradigmatic representations: substitute words for language acquisition
    (Association for Computational Linguistics (ACL), 2016) Yatbaz, Mehmet Ali; Cirik, Volkan; Department of Psychology; Department of Computer Engineering; Küntay, Aylin C.; Yüret, Deniz; Faculty Member; Faculty Member; Department of Psychology; Department of Computer Engineering; College of Social Sciences and Humanities; College of Engineering; 178879; 179996
    Learning word categories is a fundamental task in language acquisition. Previous studies show that co-occurrence patterns of preceding and following words are essential to group words into categories. However, the neighboring words, or frames, are rarely repeated exactly in the data. This creates data sparsity and hampers learning for frame based models. In this work, we propose a paradigmatic representation of word context which uses probable substitutes instead of frames. Our experiments on child-directed speech show that models based on probable substitutes learn more accurate categories with fewer examples compared to models based on frames.