13 citations · 23 across the 3 of their papers we have counts for
6 papers
Cyclical Pruning for Sparse Neural Networks
Suraj Srinivas, Andrey Kuzmin, Markus Nagel +3
Current methods for pruning neural network weights iteratively apply magnitude-based pruning on the model weights and re-train the resulting model to recover lost accuracy. In this…
A White Paper on Neural Network Quantization
Markus Nagel, Marios Fournarakis, Rana Ali Amjad +3
While neural networks have advanced the frontiers in many applications, they often come at a high computational cost. Reducing the power and latency of neural network inference is…
Bayesian Bits: Unifying Quantization and Pruning
Mart van Baalen, Christos Louizos, Markus Nagel +4
We introduce Bayesian Bits, a practical method for joint mixed precision quantization and pruning through gradient based optimization. Bayesian Bits employs a novel decomposition o…
Up or Down? Adaptive Rounding for Post-Training Quantization
Markus Nagel, Rana Ali Amjad, Mart van Baalen +2
When quantizing neural networks, assigning each floating-point weight to its nearest fixed-point value is the predominant approach. We find that, perhaps surprisingly, this is not…
Gradient Regularization for Quantization Robustness
Milad Alizadeh, Arash Behboodi, Mart van Baalen +3
We analyze the effect of quantizing weights and activations of neural networks on their loss and derive a simple regularization scheme that improves robustness against post-trainin…
Data-Free Quantization Through Weight Equalization and Bias Correction
Markus Nagel, Mart van Baalen, Tijmen Blankevoort +1
We introduce a data-free quantization method for deep neural networks that does not require fine-tuning or hyperparameter selection. It achieves near-original model performance on…