Zobrazeno 1 - 10
of 17 078
pro vyhledávání: '"Toprak, A."'
Autor:
Dogangun, Fatih, Bahar, Serdar, Yildirim, Yigit, Temir, Bora Toprak, Ugur, Emre, Dogan, Mustafa Doga
As robotics continue to enter various sectors beyond traditional industrial applications, the need for intuitive robot training and interaction systems becomes increasingly more important. This paper introduces Robotic Augmented Reality for Machine P
Externí odkaz:
http://arxiv.org/abs/2410.13412
Autor:
Kesgin, H. Toprak, Yuce, M. Kaan, Dogan, Eren, Uzun, M. Egemen, Uz, Atahan, Seyrek, H. Emre, Zeer, Ahmed, Amasyali, M. Fatih
The number of open source language models that can produce Turkish is increasing day by day, as in other languages. In order to create the basic versions of such models, the training of multilingual models is usually continued with Turkish corpora. T
Externí odkaz:
http://arxiv.org/abs/2404.17336
Autor:
Dogan, Eren, Uzun, M. Egemen, Uz, Atahan, Seyrek, H. Emre, Zeer, Ahmed, Sevi, Ezgi, Kesgin, H. Toprak, Yuce, M. Kaan, Amasyali, M. Fatih
The developments that language models have provided in fulfilling almost all kinds of tasks have attracted the attention of not only researchers but also the society and have enabled them to become products. There are commercially successful language
Externí odkaz:
http://arxiv.org/abs/2404.17010
In this work, a cell agglomeration strategy for the cut cells arising in the extended discontinuous Galerkin (XDG) method is presented. Cut cells are a fundamental aspect of unfitted mesh approaches where complex geometries or interfaces separating s
Externí odkaz:
http://arxiv.org/abs/2404.15285
This study conducts a thorough evaluation of text augmentation techniques across a variety of datasets and natural language processing (NLP) tasks to address the lack of reliable, generalized evidence for these methods. It examines the effectiveness
Externí odkaz:
http://arxiv.org/abs/2402.09141
Foundation models have recently expanded into robotics after excelling in computer vision and natural language processing. The models are accessible in two ways: open-source or paid, closed-source options. Users with access to both face a problem whe
Externí odkaz:
http://arxiv.org/abs/2402.08570
Publikováno v:
2022 Innovations in Intelligent Systems and Applications Conference (ASYU), published in IEEE Xplore
Using large training datasets enhances the generalization capabilities of neural networks. Semi-supervised learning (SSL) is useful when there are few labeled data and a lot of unlabeled data. SSL methods that use data augmentation are most successfu
Externí odkaz:
http://arxiv.org/abs/2401.01843
Publikováno v:
Communications in Computer and Information Science, vol. 1983, 450-463, Springer, 2023
Data augmentation is an effective technique for improving the performance of machine learning models. However, it has not been explored as extensively in natural language processing (NLP) as it has in computer vision. In this paper, we propose a nove
Externí odkaz:
http://arxiv.org/abs/2401.01830
Autor:
Bayar, Alperen Enes, Uyan, Ufuk, Toprak, Elif, Yuheng, Cao, Juncheng, Tang, Kindiroglu, Ahmet Alp
Urban environments are characterized by complex structures and diverse features, making accurate segmentation of point cloud data a challenging task. This paper presents a comprehensive study on the application of RandLA-Net, a state-of-the-art neura
Externí odkaz:
http://arxiv.org/abs/2312.11880
Extracellular vesicles (EVs) and plasma membrane-derived exosome-mimetic nanovesicles demonstrate significant potential for drug delivery. Latter synthetic provides higher throughput over physiological EVs. However they face size-stability and self-a
Externí odkaz:
http://arxiv.org/abs/2312.03554