12 citations · 18 across the 6 of their papers we have counts for
6 papers
Diagnosing and Repairing Factual Errors in RAG under Budget Constraints
Soroush Hashemifar, Havva Alizadeh Noughabi, Fattane Zarrinkalam +1
Retrieval-Augmented Generation (RAG) improves the factuality of large language models by grounding responses in external evidence, yet real-world deployments remain fragile. Failur…
Personalized Student Knowledge Modeling for Future Learning Resource Prediction
Soroush Hashemifar, Sherry Sahebi
Despite advances in deep learning for education, student knowledge tracing and behavior modeling face persistent challenges: limited personalization, inadequate modeling of diverse…
Mitigating Backdoors within Deep Neural Networks in Data-limited Configuration
Soroush Hashemifar, Saeed Parsa, Morteza Zakeri-Nasrabadi
As the capacity of deep neural networks (DNNs) increases, their need for huge amounts of data significantly grows. A common practice is to outsource the training process or collect…
Path Analysis for Effective Fault Localization in Deep Neural Networks
Soroush Hashemifar, Saeed Parsa, Akram Kalaee
Deep learning has revolutionized numerous fields, yet the reliability of Deep Neural Networks (DNNs) remains a concern due to their complexity and data dependency. Traditional soft…
Optimal Service Provisioning in IoT Fog-based Environment for QoS-aware Delay-sensitive Application
Soroush Hashemifar, Amir Rajabzadeh
This paper addresses the escalating challenges posed by the ever-increasing data volume, velocity, and the demand for low-latency applications, driven by the proliferation of smart…
Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstruction
Soroush Hashemifar, Abdolreza Marefat, Javad Hassannataj Joloudari +1
The small amount of training data for many state-of-the-art deep learning-based Face Recognition (FR) systems causes a marked deterioration in their performance. Although a conside…