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of 79
pro vyhledávání: '"ÇEVİKALP, Hakan"'
Autor:
Mertoğlu, Kerem, Şalk, Yusuf, Sarıkaya, Server Karahan, Turgut, Kaya, Evrenesoğlu, Yasemin, Çevikalp, Hakan, Gerek, Ömer Nezih, Dutağacı, Helin, Rousseau, David
Creation of new annotated public datasets is crucial in helping advances in 3D computer vision and machine learning meet their full potential for automatic interpretation of 3D plant models. In this paper, we introduce PLANesT-3D; a new annotated dat
Externí odkaz:
http://arxiv.org/abs/2407.21150
This paper introduces a novel approach for click-through rate (CTR) prediction within industrial recommender systems, addressing the inherent challenges of numerical imbalance and geometric asymmetry. These challenges stem from imbalanced datasets, w
Externí odkaz:
http://arxiv.org/abs/2406.03892
Multi-Task Learning (MTL) plays a crucial role in real-world advertising applications such as recommender systems, aiming to achieve robust representations while minimizing resource consumption. MTL endeavors to simultaneously optimize multiple tasks
Externí odkaz:
http://arxiv.org/abs/2406.02163
Despite much research, Graph Neural Networks (GNNs) still do not display the favorable scaling properties of other deep neural networks such as Convolutional Neural Networks and Transformers. Previous work has identified issues such as oversmoothing
Externí odkaz:
http://arxiv.org/abs/2312.10458
Autor:
Cevikalp, Hakan, Saribas, Hasan
The classification loss functions used in deep neural network classifiers can be grouped into two categories based on maximizing the margin in either Euclidean or angular spaces. Euclidean distances between sample vectors are used during classificati
Externí odkaz:
http://arxiv.org/abs/2212.11747
In this paper, we propose a new deep neural network classifier that simultaneously maximizes the inter-class separation and minimizes the intra-class variation by using the polyhedral conic classification function. The proposed method has one loss te
Externí odkaz:
http://arxiv.org/abs/2102.12570
Robust object tracking requires knowledge of tracked objects' appearance, motion and their evolution over time. Although motion provides distinctive and complementary information especially for fast moving objects, most of the recent tracking archite
Externí odkaz:
http://arxiv.org/abs/2011.09524
Convolutional Neural Networks with 3D kernels (3D-CNNs) currently achieve state-of-the-art results in video recognition tasks due to their supremacy in extracting spatiotemporal features within video frames. There have been many successful 3D-CNN arc
Externí odkaz:
http://arxiv.org/abs/2009.14639
Publikováno v:
In Pattern Recognition June 2023 138
Akademický článek
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