3 citations · 5 across the 3 of their papers we have counts for
3 papers
Hadamard Domain Training with Integers for Class Incremental Quantized Learning
Martin Schiemer, Clemens JS Schaefer, Jayden Parker Vap +4
Continual learning is a desirable feature in many modern machine learning applications, which allows in-field adaptation and updating, ranging from accommodating distribution shift…
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…
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…