| 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 | 555 | 2019 |
| 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 | 461 | 2020 |
| A white paper on neural network quantization M Nagel, M Fournarakis, RA Amjad, Y Bondarenko, M Van Baalen, ... arXiv preprint arXiv:2106.08295, 2021 | 453 | 2021 |
| 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 | 227 | 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 | 217 | 2020 |
| Relaxed quantization for discretized neural networks C Louizos, M Reisser, T Blankevoort, E Gavves, M Welling arXiv preprint arXiv:1810.01875, 2018 | 210 | 2018 |
| 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 | 125 | 2020 |
| Understanding and overcoming the challenges of efficient transformer quantization Y Bondarenko, M Nagel, T Blankevoort arXiv preprint arXiv:2109.12948, 2021 | 112 | 2021 |
| Batch-shaping for learning conditional channel gated networks BE Bejnordi, T Blankevoort, M Welling arXiv preprint arXiv:1907.06627, 2019 | 83 | 2019 |
| Differentiable joint pruning and quantization for hardware efficiency Y Wang, Y Lu, T Blankevoort European Conference on Computer Vision, 259-277, 2020 | 79 | 2020 |
| Overcoming oscillations in quantization-aware training M Nagel, M Fournarakis, Y Bondarenko, T Blankevoort International Conference on Machine Learning, 16318-16330, 2022 | 71 | 2022 |
| Gradient Regularization for Quantization Robustness M Alizadeh, A Behboodi, M Van Baalen, C Louizos, T Blankevoort, ... arXiv preprint arXiv:2002.07520, 2020 | 58 | 2020 |
| Vera: Vector-based random matrix adaptation DJ Kopiczko, T Blankevoort, YM Asano arXiv preprint arXiv:2310.11454, 2023 | 54 | 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 | 50 | 2022 |
| 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 | 49 | 2021 |
| Quantizable transformers: Removing outliers by helping attention heads do nothing Y Bondarenko, M Nagel, T Blankevoort Advances in Neural Information Processing Systems 36, 2024 | 41 | 2024 |
| Learned threshold pruning K Azarian, Y Bhalgat, J Lee, T Blankevoort arXiv preprint arXiv:2003.00075, 2020 | 40 | 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 | 30 | 2022 |
| FP8 versus INT8 for efficient deep learning inference M van Baalen, A Kuzmin, SS Nair, Y Ren, E Mahurin, C Patel, ... arXiv preprint arXiv:2303.17951, 2023 | 23 | 2023 |
| Taxonomy and evaluation of structured compression of convolutional neural networks A Kuzmin, M Nagel, S Pitre, S Pendyam, T Blankevoort, M Welling arXiv preprint arXiv:1912.09802, 2019 | 22 | 2019 |
Markus NagelQualcomm AI ResearchVerified email at qualcomm.com
