59 citations · 214 across the 24 of their papers we have counts for
4 papers · 1 filter
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…
Uncertainty for Active Learning on Graphs
Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier +2
Uncertainty Sampling is an Active Learning strategy that aims to improve the data efficiency of machine learning models by iteratively acquiring labels of data points with the high…
Structurally Prune Anything: Any Architecture, Any Framework, Any Time
Xun Wang, John Rachwan, Stephan Günnemann +1
Neural network pruning serves as a critical technique for enhancing the efficiency of deep learning models. Unlike unstructured pruning, which only sets specific parameters to zero…
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood
Rayen Dhahri, Alexander Immer, Betrand Charpentier +2
Neural network sparsification is a promising avenue to save computational time and memory costs, especially in an age where many successful AI models are becoming too large to naïv…