Post-training Quantization Methods for Deep Learning Models. - Piotr Kluska; Maciej Zieba (ACIIDS (1), 2020)

Beyond Static and Dynamic Quantization - Hybrid Quantization of Vision Transformers. - Piotr Kluska; Florian Scheidegger; A. Cristiano I. Malossi; Enrique S. Quintana-Ortí (BMVC, 2024)

QAttn: Efficient GPU Kernels for mixed-precision Vision Transformers. - Piotr Kluska; Adrián Castelló 001; Florian Scheidegger; A. Cristiano I. Malossi; Enrique S. Quintana-Ortí (CVPR Workshops, 2024)

Probabilistic Feature Matching for Fast Scalable Visual Prompting. - Thomas Frick; Cezary Skura; Filip Janicki; Roy Assaf; Niccolò Avogaro; Daniel Caraballo; Yagmur Gizem Cinar; Brown Ebouky; Ioana Giurgiu; Takayuki Katsuki; Piotr Kluska; Cristiano Malossi; et al. (IJCAI, 2024)

VP Lab: a PEFT-Enabled Visual Prompting Laboratory for Semantic Segmentation. - Niccolò Avogaro; Thomas Frick; Yagmur Gizem Cinar; Daniel Caraballo; Cezary Skura; Filip Janicki; Piotr Kluska; Brown Ebouky; Nicola Farronato; Florian Scheidegger; Cristiano Malossi; Konrad Schindler; et al. (CoRR, 2025)