| Multimodal deep learning J Ngiam, A Khosla, M Kim, J Nam, H Lee, AY Ng ICML, 2011 | 2475 | 2011 |
| On optimization methods for deep learning QV Le, J Ngiam, A Coates, A Lahiri, B Prochnow, AY Ng ICML, 2011 | 963 | 2011 |
| Gpipe: Efficient training of giant neural networks using pipeline parallelism Y Huang, Y Cheng, A Bapna, O Firat, MX Chen, D Chen, HJ Lee, J Ngiam, ... arXiv preprint arXiv:1811.06965, 2018 | 340 | 2018 |
| ICA with reconstruction cost for efficient overcomplete feature learning Q Le, A Karpenko, J Ngiam, A Ng Advances in neural information processing systems 24, 1017-1025, 2011 | 333 | 2011 |
| Tiled convolutional neural networks J Ngiam, Z Chen, D Chia, P Koh, Q Le, A Ng Advances in neural information processing systems 23, 1279-1287, 2010 | 329 | 2010 |
| Sparse filtering J Ngiam, Z Chen, S Bhaskar, P Koh, A Ng Advances in neural information processing systems 24, 1125-1133, 2011 | 249 | 2011 |
| Learning deep energy models AY Ng ICML, 2011 | 145 | 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 | 122 | 2020 |
| A Classification-Based Polyphonic Piano Transcription Approach Using Learned Feature Representations. J Nam, J Ngiam, H Lee, M Slaney Ismir, 175-180, 2011 | 96 | 2011 |
| UFLDL tutorial A Ng, J Ngiam, CY Foo, Y Mai, C Suen Chapters available at http://deeplearning. stanford. edu/wiki/index. php …, 2012 | 95 | 2012 |
| Experience is a double-edged sword: A computational model of the encoding/retrieval trade-off with familiarity LM Reder, C Paynter, RA Diana, J Ngiam, D Dickison Psychology of learning and motivation 48, 271-312, 2007 | 56 | 2007 |
| 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 | 51 | 2018 |
| 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 | 43 | 2020 |
| Condconv: Conditionally parameterized convolutions for efficient inference B Yang, G Bender, QV Le, J Ngiam arXiv preprint arXiv:1904.04971, 2019 | 33 | 2019 |
| The psychology of learning and motivation SK Reed, JA Johnsen, C Bower | 32 | 1977 |
| Unsupervised feature learning and deep learning A Ng, J Ngiam, CY Foo, Y Mai, C Suen, A Coates, A Maas, A Hannun, ... Technical report, Stanford University, 2013 | 26 | 2013 |
| Deep learning A Ng, J Ngiam, CY Foo, Y Mai CS229 Lecture Notes, 1-30, 2014 | 22 | 2014 |
| 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 | 19 | 2019 |
| Using videos to evaluate image model robustness K Gu, B Yang, J Ngiam, Q Le, J Shlens arXiv preprint arXiv:1904.10076, 2019 | 17 | 2019 |
| 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 | 10 | 2020 |
