3 papers
cs.LG2025
When to retrain a machine learning model
Regol Florence, Schwinn Leo, Sprague Kyle +2
A significant challenge in maintaining real-world machine learning models is responding to the continuous and unpredictable evolution of data. Most practitioners are faced with the…
cs.CL2024
Dynamic layer selection in decoder-only transformers
Theodore Glavas, Joud Chataoui, Florence Regol +4
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-…
cs.LG2024
Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE
Florence Regol, Joud Chataoui, Bertrand Charpentier +3
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 com…