Data-free quantization through weight equalization and bias correction M Nagel, M Baalen, T Blankevoort, M Welling
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
630 2019 A white paper on neural network quantization M Nagel, M Fournarakis, RA Amjad, Y Bondarenko, M Van Baalen, ...
arXiv preprint arXiv:2106.08295, 2021
618 2021 Up or down? adaptive rounding for post-training quantization M Nagel, RA Amjad, M Van Baalen, C Louizos, T Blankevoort
International Conference on Machine Learning, 7197-7206, 2020
588 2020 Lsq+: Improving low-bit quantization through learnable offsets and better initialization Y Bhalgat, J Lee, M Nagel, T Blankevoort, N Kwak
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
263 2020 Conditional channel gated networks for task-aware continual learning D Abati, J Tomczak, T Blankevoort, S Calderara, R Cucchiara, ...
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
244 2020 Relaxed quantization for discretized neural networks C Louizos, M Reisser, T Blankevoort, E Gavves, M Welling
arXiv preprint arXiv:1810.01875, 2018
218 2018 Understanding and overcoming the challenges of efficient transformer quantization Y Bondarenko, M Nagel, T Blankevoort
arXiv preprint arXiv:2109.12948, 2021
143 2021 Bayesian bits: Unifying quantization and pruning M Van Baalen, C Louizos, M Nagel, RA Amjad, Y Wang, T Blankevoort, ...
Advances in neural information processing systems 33, 5741-5752, 2020
143 2020 Vera: Vector-based random matrix adaptation DJ Kopiczko, T Blankevoort, YM Asano
arXiv preprint arXiv:2310.11454, 2023
134 2023 Overcoming oscillations in quantization-aware training M Nagel, M Fournarakis, Y Bondarenko, T Blankevoort
International Conference on Machine Learning, 16318-16330, 2022
106 2022 Batch-shaping for learning conditional channel gated networks BE Bejnordi, T Blankevoort, M Welling
arXiv preprint arXiv:1907.06627, 2019
89 2019 Differentiable joint pruning and quantization for hardware efficiency Y Wang, Y Lu, T Blankevoort
European Conference on Computer Vision, 259-277, 2020
86 2020 Quantizable transformers: Removing outliers by helping attention heads do nothing Y Bondarenko, M Nagel, T Blankevoort
Advances in Neural Information Processing Systems 36, 75067-75096, 2023
74 2023 Fp8 quantization: The power of the exponent A Kuzmin, M Van Baalen, Y Ren, M Nagel, J Peters, T Blankevoort
Advances in Neural Information Processing Systems 35, 14651-14662, 2022
73 2022 Gradient M Alizadeh, A Behboodi, M Van Baalen, C Louizos, T Blankevoort, ...
arXiv preprint arXiv:2002.07520, 2020
63 2020 Distilling optimal neural networks: Rapid search in diverse spaces B Moons, P Noorzad, A Skliar, G Mariani, D Mehta, C Lott, T Blankevoort
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
54 2021 Pruning vs quantization: Which is better? A Kuzmin, M Nagel, M Van Baalen, A Behboodi, T Blankevoort
Advances in neural information processing systems 36, 62414-62427, 2023
48 2023 SpinQuant--LLM quantization with learned rotations Z Liu, C Zhao, I Fedorov, B Soran, D Choudhary, R Krishnamoorthi, ...
arXiv preprint arXiv:2405.16406, 2024
43 2024 Learned threshold pruning K Azarian, Y Bhalgat, J Lee, T Blankevoort
arXiv preprint arXiv:2003.00075, 2020
42 2020 Neural network quantization with ai model efficiency toolkit (aimet) S Siddegowda, M Fournarakis, M Nagel, T Blankevoort, C Patel, ...
arXiv preprint arXiv:2201.08442, 2022
37 2022 
Markus NagelQualcomm AI Researchqualcomm.com üzerinde doğrulanmış e-posta adresine sahip
