| A white paper on neural network quantization M Nagel, M Fournarakis, RA Amjad, Y Bondarenko, M Van Baalen, ... arXiv preprint arXiv:2106.08295, 2021 | 321 | 2021 |
| Overcoming oscillations in quantization-aware training M Nagel, M Fournarakis, Y Bondarenko, T Blankevoort International Conference on Machine Learning, 16318-16330, 2022 | 50 | 2022 |
| 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 | 24 | 2022 |
| In-hindsight quantization range estimation for quantized training M Fournarakis, M Nagel Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 6 | 2021 |
| Quantization robust federated learning for efficient inference on heterogeneous devices K Gupta, M Fournarakis, M Reisser, C Louizos, M Nagel arXiv preprint arXiv:2206.10844, 2022 | 5 | 2022 |
| Softmax bias correction for quantized generative models NP Pandey, M Fournarakis, C Patel, M Nagel Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 3 | 2023 |
| QBitOpt: Fast and Accurate Bitwidth Reallocation during Training J Peters, M Fournarakis, M Nagel, M Van Baalen, T Blankevoort Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 1 | 2023 |
| Quantizing Neural Networks for Low-Power Computer Vision M Fournarakis, M Nagel, RA Amjad, Y Bondarenko, M van Baalen, ... Low-Power Computer Vision, 235-272, 2022 | | 2022 |
Markus NagelQualcomm AI Researchqualcomm.com의 이메일 확인됨
