Enhanced Expressivity in Graph Neural Networks with Lanczos-Based Linear Constraints

Autor: Azizi, Niloofar, Kriege, Nils, Bischof, Horst

Graph Neural Networks (GNNs) excel in handling graph-structured data but often underperform in link prediction tasks compared to classical methods, mainly due to the limitations of the commonly used Message Passing GNNs (MPNNs). Notably, their abilit

Externí odkaz: http://arxiv.org/abs/2408.12334

3D Human Pose Estimation Using M\'obius Graph Convolutional Networks

Autor: Azizi, Niloofar, Possegger, Horst, Rodolà, Emanuele, Bischof, Horst

3D human pose estimation is fundamental to understanding human behavior. Recently, promising results have been achieved by graph convolutional networks (GCNs), which achieve state-of-the-art performance and provide rather light-weight architectures.

Externí odkaz: http://arxiv.org/abs/2203.10554

Location Dependency in Video Prediction

Autor: Azizi, Niloofar, Farazi, Hafez, Behnke, Sven

Publikováno v: International Conference on Artificial Neural Networks. Springer, Cham, 2018

Deep convolutional neural networks are used to address many computer vision problems, including video prediction. The task of video prediction requires analyzing the video frames, temporally and spatially, and constructing a model of how the environm

Externí odkaz: http://arxiv.org/abs/1810.04937

Evaluation of Microhardness of Mineral Trioxide Aggregate After Immediate Placement of Different Coronal Restorations: An In Vitro Study.

Autor: Kazemipoor M; Assistant Professor, Department of Endodontics, School of Dentistry, Shahid Sadoughi University of Medical Sciences, Yazd, Iran., Azizi N; Dentist, Private Practice, Yazd, Iran., Farahat F; Assistant Professor, Department of Operative Dentistry, School of Dentistry, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.

Publikováno v: Journal of dentistry (Tehran, Iran) [J Dent (Tehran)] 2018 Mar; Vol. 15 (2), pp. 116-122.