163 citations · 724 across the 59 of their papers we have counts for
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Deep Graph Generators: A Survey
Faezeh Faez, Yassaman Ommi, Mahdieh Soleymani Baghshah +1
Deep generative models have achieved great success in areas such as image, speech, and natural language processing in the past few years. Thanks to the advances in graph-based deep…
On the Observability and Controllability of Large-Scale IoT Networks: Reducing Number of Unmatched Nodes via Link Addition
Mohammadreza Doostmohammadian, Hamid R. Rabiee
In this paper, we study large-scale networks in terms of observability and controllability. In particular, we compare the number of unmatched nodes in two main types of Scale-Free…
Multiresolution Knowledge Distillation for Anomaly Detection
Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh +2
Unsupervised representation learning has proved to be a critical component of anomaly detection/localization in images. The challenges to learn such a representation are two-fold.…
Accurate and Rapid Diagnosis of COVID-19 Pneumonia with Batch Effect Removal of Chest CT-Scans and Interpretable Artificial Intelligence
Rassa Ghavami Modegh, Mehrab Hamidi, Saeed Masoudian +22
COVID-19 is a virus with high transmission rate that demands rapid identification of the infected patients to reduce the spread of the disease. The current gold-standard test, Reve…
Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization
Maryam Ramezani, Aryan Ahadinia, Amirmohammad Ziaei +1
Access to complete data in large-scale networks is often infeasible. Therefore, the problem of missing data is a crucial and unavoidable issue in the analysis and modeling of real-…
Puzzle-AE: Novelty Detection in Images through Solving Puzzles
Mohammadreza Salehi, Ainaz Eftekhar, Niousha Sadjadi +2
Autoencoder, as an essential part of many anomaly detection methods, is lacking flexibility on normal data in complex datasets. U-Net is proved to be effective for this purpose but…
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection
Mohammadreza Salehi, Atrin Arya, Barbod Pajoum +4
Autoencoders (AE) have recently been widely employed to approach the novelty detection problem. Trained only on the normal data, the AE is expected to reconstruct the normal data e…