Jiquan Ngiam
Jiquan Ngiam
Google Brain
Verified email at google.com - Homepage
Cited by
Cited by
Multimodal deep learning
J Ngiam, A Khosla, M Kim, J Nam, H Lee, AY Ng
ICML, 2011
On optimization methods for deep learning
QV Le, J Ngiam, A Coates, A Lahiri, B Prochnow, AY Ng
ICML, 2011
Tiled convolutional neural networks
J Ngiam, Z Chen, D Chia, PW Koh, QV Le, AY Ng
Advances in neural information processing systems, 1279-1287, 2010
ICA with reconstruction cost for efficient overcomplete feature learning
QV Le, A Karpenko, J Ngiam, AY Ng
Advances in neural information processing systems, 1017-1025, 2011
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, 103-112, 2019
Sparse filtering
J Ngiam, Z Chen, SA Bhaskar, PW Koh, AY Ng
Advances in neural information processing systems, 1125-1133, 2011
Learning deep energy models
J Ngiam, Z Chen, PW Koh, AY Ng
Proceedings of the 28th international conference on machine learning (ICML …, 2011
A Classification-Based Polyphonic Piano Transcription Approach Using Learned Feature Representations.
J Nam, J Ngiam, H Lee, M Slaney
Ismir, 175-180, 2011
UFLDL tutorial
A Ng, J Ngiam, CY Foo, Y Mai, C Suen
2012)[2014-08-12]. http://deeplearning. stanford. edu/wiki/index. php …, 2010
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
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
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
The psychology of learning and motivation
SK Reed, JA Johnsen, C Bower
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
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
Deep learning
A Ng, J Ngiam, CY Foo, Y Mai
CS229 Lecture Notes, 1-30, 2014
Condconv: Conditionally parameterized convolutions for efficient inference
B Yang, G Bender, QV Le, J Ngiam
Advances in Neural Information Processing Systems, 1307-1318, 2019
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
Using videos to evaluate image model robustness
K Gu, B Yang, J Ngiam, Q Le, J Shlens
arXiv preprint arXiv:1904.10076, 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, ...
arXiv preprint arXiv:2004.00831, 2020
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