| Multimodal deep learning. J Ngiam, A Khosla, M Kim, J Nam, H Lee, AY Ng ICML 11, 689-696, 2011 | 4787 | 2011 |
| Scalability in perception for autonomous driving: Waymo open dataset P Sun, H Kretzschmar, X Dotiwalla, A Chouard, V Patnaik, P Tsui, J Guo, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 4673 | 2020 |
| Gpipe: Efficient training of giant neural networks using pipeline parallelism Y Huang, Y Cheng, A Bapna, O Firat, D Chen, M Chen, HJ Lee, J Ngiam, ... Advances in neural information processing systems 32, 2019 | 2400 | 2019 |
| On optimization methods for deep learning QV Le, J Ngiam, A Coates, A Lahiri, B Prochnow, AY Ng Proceedings of the 28th international conference on international conference …, 2011 | 1442 | 2011 |
| Condconv: Conditionally parameterized convolutions for efficient inference B Yang, G Bender, QV Le, J Ngiam Advances in neural information processing systems 32, 2019 | 1018 | 2019 |
| Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset S Ettinger, S Cheng, B Caine, C Liu, H Zhao, S Pradhan, Y Chai, B Sapp, ... Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 849 | 2021 |
| Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection Y Li, AW Yu, T Meng, B Caine, J Ngiam, D Peng, J Shen, Y Lu, D Zhou, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 651 | 2022 |
| End-to-end multi-view fusion for 3d object detection in lidar point clouds Y Zhou, P Sun, Y Zhang, D Anguelov, J Gao, T Ouyang, J Guo, J Ngiam, ... Conference on Robot Learning, 923-932, 2020 | 514 | 2020 |
| Tiled convolutional neural networks J Ngiam, Z Chen, D Chia, P Koh, Q Le, A Ng Advances in neural information processing systems 23, 2010 | 486 | 2010 |
| Scene transformer: A unified architecture for predicting multiple agent trajectories J Ngiam, B Caine, V Vasudevan, Z Zhang, HTL Chiang, J Ling, R Roelofs, ... arXiv preprint arXiv:2106.08417, 2021 | 472 | 2021 |
| ICA with reconstruction cost for efficient overcomplete feature learning Q Le, A Karpenko, J Ngiam, A Ng Advances in neural information processing systems 24, 2011 | 471 | 2011 |
| Sparse filtering J Ngiam, Z Chen, S Bhaskar, P Koh, A Ng Advances in neural information processing systems 24, 2011 | 392 | 2011 |
| Just pick a sign: Optimizing deep multitask models with gradient sign dropout Z Chen, J Ngiam, Y Huang, T Luong, H Kretzschmar, Y Chai, D Anguelov Advances in Neural Information Processing Systems 33, 2039-2050, 2020 | 305 | 2020 |
| Learning deep energy models J Ngiam, Z Chen, PW Koh, AY Ng Proceedings of the 28th international conference on machine learning (ICML …, 2011 | 251 | 2011 |
| Starnet: Targeted computation for object detection in point clouds J Ngiam, B Caine, W Han, B Yang, Y Chai, P Sun, Y Zhou, X Yi, O Alsharif, ... arXiv preprint arXiv:1908.11069, 2019 | 160 | 2019 |
| Domain adaptive transfer learning with specialist models J Ngiam, D Peng, V Vasudevan, S Kornblith, QV Le, R Pang arXiv preprint arXiv:1811.07056, 2018 | 150 | 2018 |
| 3d-man: 3d multi-frame attention network for object detection Z Yang, Y Zhou, Z Chen, J Ngiam Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 148 | 2021 |
| A Classification-Based Polyphonic Piano Transcription Approach Using Learned Feature Representations. J Nam, J Ngiam, H Lee, M Slaney Ismir, 175-180, 2011 | 121 | 2011 |
| UFLDL tutorial A Ng, J Ngiam, CY Foo, Y Mai, C Suen Chapters available at http://deeplearningstanford. edu/wiki/index. php …, 2012 | 111 | 2012 |
| Improving 3d object detection through progressive population based augmentation S Cheng, Z Leng, ED Cubuk, B Zoph, C Bai, J Ngiam, Y Song, B Caine, ... European Conference on Computer Vision, 279-294, 2020 | 99 | 2020 |
