Bağlantısal Dilbilgisi ve Belirtmesizlik Kuramı

Date

2000-01-01

Author

Pamir Dietrich, Ayşe

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http://katalog.tdk.gov.tr/search?query=Ba%C4%9Flant%C4%B1sal%20dilbilgisi%20ve%20belirtmesizlik%20kuram%C4%B1&field=title&isOriginal=false
https://hdl.handle.net/11511/94088

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Ankara universitesi Tömer Dil Dergisi

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Department of History, Article

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Bağlantısal Dilbilim Terimleri Sözlüğü Pamir Dietrich, Ayşe (1998-01-01)
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Connectionist multi-sequence modelling and applications to multilingual neural machine translation Fırat, Orhan; Yarman Vural, Fatoş Tunay; Cho, Kyunghyun; Department of Computer Engineering (2017) Deep (recurrent) neural networks has been shown to successfully learn complex mappings between arbitrary length input and output sequences, called sequence to sequence learning, within the effective framework of encoder-decoder networks. This thesis investigates the extensions of sequence to sequence models, to handle multiple sequences at the same time within a single parametric model, and proposes the ﬁrst large scale connectionist multi-sequence modeling approach. The proposed multisequence modeling arch...
Development of a pcr-based specific method for the detection of aspergillus fumigatus by random cdna cloning Söyler, Alper; Ögel, Zümrüt Begüm; Department of Food Engineering (2004) Aspergillus fumigatus is a foodborne and airborne human pathogen which produces mycotoxins such as gliotoxin, helvolic acid, fumigallin, and fumigaclavin. The most common disease caused by A. fumigatus is aspergillosis, which is often fatal, especially among AIDS, cancer, and organ-transplant patients. In this study, random cDNA cloning was performed from a cDNA library of A. fumigatus (IMI 385708) constructed on ? ZAP Express. Some of these clones were selected according to their insert sizes and were furt...
Object Augmentation for Out-of-Context Object Recognition Eryüksel, Oğul Can; Kalkan, Sinan; Department of Computer Engineering (2022-5-18) The visual context in an image contains rich information about and between foreground objects and the background. Deep learning models learn contextual information implicitly in general. However, since training datasets generally do not include all possible contexts, deep models tend to memorize contextual details. This can lead to poor recognition performance when models are deployed in real-world applications since objects may appear in unexpected contexts or places. These types of objects are called out-...

Citation Formats

A. Pamir Dietrich, “Bağlantısal Dilbilgisi ve Belirtmesizlik Kuramı,” Ankara universitesi Tömer Dil Dergisi, vol. 9, pp. 18–24, 2000, Accessed: 00, 2021. [Online]. Available: http://katalog.tdk.gov.tr/search?query=Ba%C4%9Flant%C4%B1sal%20dilbilgisi%20ve%20belirtmesizlik%20kuram%C4%B1&field=title&isOriginal=false.