van Baalen, Mart

A White Paper on Neural Network Quantization

Nagel, M., Fournarakis, M., Amjad, R. A., Bondarenko, Y., van Baalen, M., & Blankevoort, T. (2021) · arXiv

Advanced Neural Network ApplicationsLow-power high-performance VLSI design

Cited by 323

Up or Down? Adaptive Rounding for Post-Training Quantization

Nagel, M., Amjad, R. A., van Baalen, M., Louizos, C., & Blankevoort, T. (2020) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 252

FP8 Quantization: The Power of the Exponent

Kuzmin, A., Van Baalen, M., Ren, Y., Nagel, M., Peters, J., & Blankevoort, T. (2022) · arXiv

Numerical Methods and AlgorithmsLow-power high-performance VLSI design

Cited by 24

FP8 versus INT8 for efficient deep learning inference

van Baalen, M., Kuzmin, A., Nair, S. S., Ren, Y., Mahurin, E., Patel, C., Subramanian, S., Lee, S., Nagel, M., Soriaga, J., et al. (2023) · arXiv

Advanced Neural Network ApplicationsNumerical Methods and Algorithms

Cited by 24

Gradient $\ell_1$ Regularization for Quantization Robustness

Alizadeh, M., Behboodi, A., van Baalen, M., Louizos, C., Blankevoort, T., & Welling, M. (2020) · arXiv

Advanced Neural Network ApplicationsStochastic Gradient Optimization Techniques

Cited by 10