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pro vyhledávání: '"Chataoui, Joud"'
Autor:
Glavas, Theodore, Chataoui, Joud, Regol, Florence, Jabbour, Wassim, Valkanas, Antonios, Oreshkin, Boris N., Coates, Mark
The vast size of Large Language Models (LLMs) has prompted a search to optimize inference. One effective approach is dynamic inference, which adapts the architecture to the sample-at-hand to reduce the overall computational cost. We empirically exami
Externí odkaz:
http://arxiv.org/abs/2410.20022
Autor:
Regol, Florence, Chataoui, Joud, Charpentier, Bertrand, Coates, Mark, Piantanida, Pablo, Gunnemann, Stephan
Machine learning models can solve complex tasks but often require significant computational resources during inference. This has led to the development of various post-training computation reduction methods that tackle this issue in different ways, s
Externí odkaz:
http://arxiv.org/abs/2406.14404
Large pretrained models, coupled with fine-tuning, are slowly becoming established as the dominant architecture in machine learning. Even though these models offer impressive performance, their practical application is often limited by the prohibitiv
Externí odkaz:
http://arxiv.org/abs/2310.09163