activity
20222025
most citedRe-ranking the Context for Multimodal Retrieval Augmented Generation

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20252 cited

Re-ranking the Context for Multimodal Retrieval Augmented Generation

Matin Mortaheb, Mohammad A. Amir Khojastepour, Srimat T. Chakradhar +1

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge to generate a response within a context with improved accuracy and re…

cs.LG2025

RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance

Matin Mortaheb, Mohammad A. Amir Khojastepour, Srimat T. Chakradhar +1

Retrieval-augmented generation (RAG) improves large language models (LLMs) by using external knowledge to guide response generation, reducing hallucinations. However, RAG, particul…

cs.CV20241 cited

Transformer-Aided Semantic Communications

Matin Mortaheb, Erciyes Karakaya, Mohammad A. Amir Khojastepour +1

The transformer structure employed in large language models (LLMs), as a specialized category of deep neural networks (DNNs) featuring attention mechanisms, stands out for their ab…

eess.IV2023

Deep Learning-Based Real-Time Quality Control of Standard Video Compression for Live Streaming

Matin Mortaheb, Mohammad A. Amir Khojastepour, Srimat T. Chakradhar +1

Ensuring high-quality video content for wireless users has become increasingly vital. Nevertheless, maintaining a consistent level of video quality faces challenges due to the fluc…

cs.IT2022

Codebook Design for Composite Beamforming in Next-generation mmWave Systems

Nariman Torkzaban, Mohamamd A., Khojastepour +1

In pursuance of the unused spectrum in higher frequencies, millimeter wave (mmWave) bands have a pivotal role. However, the high path-loss and poor scattering associated with mmWav…