163 citations · 724 across the 59 of their papers we have counts for
67 papers
CRISPR: Ensemble Model
Mohammad Rostami, Amin Ghariyazi, Hamed Dashti +2
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a gene editing technology that has revolutionized the fields of biology and medicine. However, one of the chal…
Epsilon non-Greedy: A Bandit Approach for Unbiased Recommendation via Uniform Data
S. M. F. Sani, Seyed Abbas Hosseini, Hamid R. Rabiee
Often, recommendation systems employ continuous training, leading to a self-feedback loop bias in which the system becomes biased toward its previous recommendations. Recent studie…
DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property
Maryam Ramezani, Aryan Ahadinia, Erfan Farhadi +1
The fast growth of social networks and their data access limitations in recent years has led to increasing difficulty in obtaining the complete topology of these networks. However,…
ClusterSeq: Enhancing Sequential Recommender Systems with Clustering based Meta-Learning
Mohammmadmahdi Maheri, Reza Abdollahzadeh, Bardia Mohammadi +3
In practical scenarios, the effectiveness of sequential recommendation systems is hindered by the user cold-start problem, which arises due to limited interactions for accurately d…
Enhancing the Prediction of Emotional Experience in Movies using Deep Neural Networks: The Significance of Audio and Language
Sogand Mehrpour Mohammadi, Meysam Gouran Orimi, Hamidreza Rabiee
Our paper focuses on making use of deep neural network models to accurately predict the range of human emotions experienced during watching movies. In this certain setup, there exi…
Network-based Control of Epidemic via Flattening the Infection Curve: High-Clustered vs. Low-Clustered Social Networks
Mohammadreza Doostmohammadian, Hamid R. Rabiee
Recent studies in network science and control have shown a meaningful relationship between the epidemic processes (e.g., COVID-19 spread) and some network properties. This paper st…
Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey
Gita Sarafraz, Armin Behnamnia, Mehran Hosseinzadeh +3
Machine learning algorithms have revolutionized different fields, including natural language processing, computer vision, signal processing, and medical data processing. Despite th…
SCGG: A Deep Structure-Conditioned Graph Generative Model
Faezeh Faez, Negin Hashemi Dijujin, Mahdieh Soleymani Baghshah +1
Deep learning-based graph generation approaches have remarkable capacities for graph data modeling, allowing them to solve a wide range of real-world problems. Making these methods…
SOInter: A Novel Deep Energy Based Interpretation Method for Explaining Structured Output Models
S. Fatemeh Seyyedsalehi, Mahdieh Soleymani, Hamid R. Rabiee
We propose a novel interpretation technique to explain the behavior of structured output models, which learn mappings between an input vector to a set of output variables simultane…
LAP: An Attention-Based Module for Concept Based Self-Interpretation and Knowledge Injection in Convolutional Neural Networks
Rassa Ghavami Modegh, Ahmad Salimi, Alireza Dizaji +1
Despite the state-of-the-art performance of deep convolutional neural networks, they are susceptible to bias and malfunction in unseen situations. Moreover, the complex computation…
FNR: A Similarity and Transformer-Based Approach to Detect Multi-Modal Fake News in Social Media
Faeze Ghorbanpour, Maryam Ramezani, Mohammad A. Fazli +1
The availability and interactive nature of social media have made them the primary source of news around the globe. The popularity of social media tempts criminals to pursue their…
RC-RNN: Reconfigurable Cache Architecture for Storage Systems Using Recurrent Neural Networks
Shahriar Ebrahimi, Reza Salkhordeh, Seyed Ali Osia +3
Solid-State Drives (SSDs) have significant performance advantages over traditional Hard Disk Drives (HDDs) such as lower latency and higher throughput. Significantly higher price p…
CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation
Yassaman Ommi, Matin Yousefabadi, Faezeh Faez +3
Graph data structures are fundamental for studying connected entities. With an increase in the number of applications where data is represented as graphs, the problem of graph gene…
Distributed Detection and Mitigation of Biasing Attacks over Multi-Agent Networks
Mohammadreza Doostmohammadian, Houman Zarrabi, Hamid R. Rabiee +2
This paper proposes a distributed attack detection and mitigation technique based on distributed estimation over a multi-agent network, where the agents take partial system measure…
Analysis of Contractions in System Graphs: Application to State Estimation
Mohammadreza Doostmohammadian, Themistoklis Charalambous, Miadreza Shafie-khah +2
Observability and estimation are closely tied to the system structure, which can be visualized as a system graph--a graph that captures the inter-dependencies within the state vari…
SINA-BERT: A pre-trained Language Model for Analysis of Medical Texts in Persian
Nasrin Taghizadeh, Ehsan Doostmohammadi, Elham Seifossadat +2
We have released Sina-BERT, a language model pre-trained on BERT (Devlin et al., 2018) to address the lack of a high-quality Persian language model in the medical domain. SINA-BERT…
GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference
Mahsa Ghorbani, Mojtaba Bahrami, Anees Kazi +3
The increased amount of multi-modal medical data has opened the opportunities to simultaneously process various modalities such as imaging and non-imaging data to gain a comprehens…
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI
Reza Shirkavand, Sana Ayromlou, Soroush Farghadani +7
Early detection of dementia through specific biomarkers in MR images plays a critical role in developing support strategies proactively. Fazekas scale facilitates an accurate quant…
RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data
Mahsa Ghorbani, Anees Kazi, Mahdieh Soleymani Baghshah +2
Disease prediction is a well-known classification problem in medical applications. GCNs provide a powerful tool for analyzing the patients' features relative to each other. This ca…
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-…