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cs.LG2026
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…
cs.LG2026
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…