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Nicholas Fraser

2 accepted papers

2026

MixQuant: Pushing the Limits of Block Rotations in Post-Training Quantization

ICML 2026poster

Recent post-training quantization (PTQ) methods have adopted block rotations to diffuse outliers prior to rounding. While this reduces the overhead of full-vector rotations, the effect of block structure on outlier suppression remains poorly understood. To fill this gap, we present the first systema…

Cited by 0SourceScholar
2018

SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks

CVPR 2018poster

Inference for state-of-the-art deep neural networks is computationally expensive, making them difficult to deploy on constrained hardware environments. An efficient way to reduce this complexity is to quantize the weight parameters and/or activations during training by approximating their distributi…