| Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors CN Coelho, A Kuusela, S Li, H Zhuang, J Ngadiuba, TK Aarrestad, ... Nature Machine Intelligence 3 (8), 675-686, 2021 | 167 | 2021 |
| Automatic deep heterogeneous quantization of deep neural networks for ultra low-area, low-latency inference on the edge at particle colliders CN Coelho, A Kuusela, S Li, H Zhuang, T Aarrestad, V Loncar, ... arXiv preprint arXiv:2006.10159 6, 2020 | 28 | 2020 |
| Efficient use of quantization parameters in machine-learning models for video coding C Coelho, D He, A Kuusela, S Li US Patent 10,674,152, 2020 | 22 | 2020 |
| Receptive-field-conforming convolution models for video coding S Li, C Coelho, A Kuusela, D He US Patent 11,025,907, 2021 | 16 | 2021 |
| Receptive-field-conforming convolutional models for video coding C Coelho, A Kuusela, S Li, D He US Patent 10,869,036, 2020 | 14 | 2020 |
| Using rate distortion cost as a loss function for deep learning C Coelho, A Kuusela, J Young, S Li, D He US Patent 11,956,447, 2024 | 2 | 2024 |
| Ultra Light Models and Decision Fusion for Fast Video Coding S Li, C Coelho, IS Chong, A Kuusela US Patent App. 17/779,380, 2023 | 1 | 2023 |
| Efficient use of quantization parameters in machine-learning models for video coding C Coelho, D He, A Kuusela, S Li US Patent 11,310,501, 2022 | 1 | 2022 |
| Automatic Selection of Quantization and Filter Pruning Optimization Under Energy Constraints CJN Coelho, P Zielinski, A Kuusela, S Li, H Zhuang US Patent App. 18/007,871, 2023 | | 2023 |
| Receptive-field-conforming convolutional models for video coding C Coelho, A Kuusela, S Li, D He US Patent 11,310,498, 2022 | | 2022 |
