2 citations · 3 across the 15 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…