3 citations · 5 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 2 cited
Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization
Clemens JS Schaefer, Navid Lambert-Shirzad, Xiaofan Zhang +7
Efficiently serving neural network models with low latency is becoming more challenging due to increasing model complexity and parameter count. Model quantization offers a solution…
cs.LG2023★ 3 cited
Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search
Clemens JS Schaefer, Elfie Guo, Caitlin Stanton +7
Serving large-scale machine learning (ML) models efficiently and with low latency has become challenging owing to increasing model size and complexity. Quantizing models can simult…