8 papers
Unifying Data, Memory, and Compute Efficiency in LLM training: A Survey
Vanessa Schmidt, Huy Hoang Nguyen, Cédric Jung +2
Resource constraints increasingly determine what can be trained, fine-tuned, and deployed in large language models (LLMs), yet efficiency is often studied through isolated techniqu…
Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness
Murilo Batista, Shirin Salehi, Saeed Mashdour +3
Building on recent advances in representation learning for wireless channels, this work investigates the cost-benefit trade-offs of high-dimensional channel embeddings in practical…
A Full Compression Pipeline for Green Federated Learning in Communication-Constrained Environments
Elouan Colybes, Shirin Salehi, Anke Schmeink
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, thereby preserving privacy. However, FL often suffers from signifi…
Conformal Cross-Modal Active Learning
Huy Hoang Nguyen, Cédric Jung, Shirin Salehi +3
Foundation models for vision have transformed visual recognition with powerful pretrained representations and strong zero-shot capabilities, yet their potential for data-efficient…
Active Learning Using Aggregated Acquisition Functions: Accuracy and Sustainability Analysis
Cédric Jung, Shirin Salehi, Anke Schmeink
Active learning (AL) is a machine learning (ML) approach that strategically selects the most informative samples for annotation during training, aiming to minimize annotation costs…
Study of Robust Power Allocation for User-Centric Cell-Free Massive MIMO Networks
Saeed Mashdour, Saeed Mohammadzadeh, André R. Flores +3
In cell-free massive multiple-input multiple-output (MIMO) networks, robust resource allocation is critical to ensure reliable system performance in the presence of channel uncerta…