2 citations · 3 across the 15 of their papers we have counts for
16 papers
Semantic-Aware Neural Video Codec for Error-Resilient Low-Latency Transmission
Matin Mortaheb, Homa Esfahanizadeh, Jinfeng Du +1
Emerging physical AI systems require low-latency, task-oriented video communication over unreliable channels. We propose a semantic-aware multi-level neural video coding method for…
UniTAC: Universal Task-Aware Compression via Weighted Distortion Measures
Homa Esfahanizadeh, Matin Mortaheb, Jinfeng Du +2
Lossy compression is conventionally driven by a task-agnostic distortion (e.g., MSE or MS-SSIM), yet in many emerging applications the receiver cares not about uniform fidelity but…
Towards Robust Semantic Video Transmission over Block Erasure Channels
Nargis Fayaz, Homa Esfahanizadeh, Matin Mortaheb +2
This paper investigates semantic-aware neural joint source-channel coding (JSCC) for robust video transmission over block erasure channels. We propose a neural video compression fr…
Multi-Modal Semantic Communication
Matin Mortaheb, Erciyes Karakaya, Sennur Ulukus
Semantic communication aims to transmit information most relevant to a task rather than raw data, offering significant gains in communication efficiency for applications such as te…
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…
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…
Efficient Semantic Communication Through Transformer-Aided Compression
Matin Mortaheb, Mohammad A. Amir Khojastepour, Sennur Ulukus
Transformers, known for their attention mechanisms, have proven highly effective in focusing on critical elements within complex data. This feature can effectively be used to addre…
Age-: Communication-Efficient Federated Learning Using Age Factor
Matin Mortaheb, Priyanka Kaswan, Sennur Ulukus
Federated learning (FL) is a collaborative approach where multiple clients, coordinated by a parameter server (PS), train a unified machine-learning model. The approach, however, s…
Age of Gossip in Networks with Multiple Views of a Source
Kian J. Khojastepour, Matin Mortaheb, Sennur Ulukus
We consider the version age of information (AoI) in a network where a subset of nodes act as sensing nodes, sampling a source that in general can follow a continuous distribution.…
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…
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…
Deep Learning-Based Real-Time Rate Control for Live Streaming on Wireless Networks
Matin Mortaheb, Mohammad A. Amir Khojastepour, Srimat T. Chakradhar +1
Providing wireless users with high-quality video content has become increasingly important. However, ensuring consistent video quality poses challenges due to variable encoded bitr…
Semantic Multi-Resolution Communications
Matin Mortaheb, Mohammad A. Amir Khojastepour, Srimat T. Chakradhar +1
Deep learning based joint source-channel coding (JSCC) has demonstrated significant advancements in data reconstruction compared to separate source-channel coding (SSCC). This supe…
Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs
Matin Mortaheb, Sennur Ulukus
Decentralized and federated learning algorithms face data heterogeneity as one of the biggest challenges, especially when users want to learn a specific task. Even when personalize…
Hierarchical Over-the-Air FedGradNorm
Cemil Vahapoglu, Matin Mortaheb, Sennur Ulukus
Multi-task learning (MTL) is a learning paradigm to learn multiple related tasks simultaneously with a single shared network where each task has a distinct personalized header netw…
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning
Matin Mortaheb, Cemil Vahapoglu, Sennur Ulukus
Multi-task learning (MTL) is a novel framework to learn several tasks simultaneously with a single shared network where each task has its distinct personalized header network for f…