26 citations · 53 across the 7 of their papers we have counts for
3 papers · 1 filter
STaMP: Sequence Transformation and Mixed Precision for Low-Precision Activation Quantization
Marco Federici, Riccardo Del Chiaro, Boris van Breugel +2
Quantization is the key method for reducing inference latency, power and memory footprint of generative AI models. However, accuracy often degrades sharply when activations are qua…
HadaNorm: Diffusion Transformer Quantization through Mean-Centered Transformations
Marco Federici, Riccardo Del Chiaro, Boris van Breugel +2
Diffusion models represent the cutting edge in image generation, but their high memory and computational demands hinder deployment on resource-constrained devices. Post-Training Qu…
Position: All Current Generative Fidelity and Diversity Metrics are Flawed
Ossi Räisä, Boris van Breugel, Mihaela van der Schaar
Any method's development and practical application is limited by our ability to measure its reliability. The popularity of generative modeling emphasizes the importance of good syn…