Jiquan Ngiam

B.S. Computer Science | Carnegie Mellon University
Ph.D. Student (advisor: Andrew Ng)
AI Lab
Stanford University

jngiam

cs.stanford.edu

Research Interests

Machine learning, Deep learning, Unsupervised feature learning, Computer vision, Multimodal feature learning.

Recent Publications

J. Ngiam, P. Koh, Z. Chen, S. Bhaskar, A.Y. Ng.
Sparse filtering.
NIPS 2011. [PDF] [Supplementary]
Q.V. Le, A. Karpenko, J. Ngiam, A.Y. Ng.
ICA with reconstruction cost for efficient overcomplete feature learning.
NIPS 2011. [PDF] [Supplementary]
J. Nam, J. Ngiam, H. Lee, M. Slaney.
A classification-based polyphonic piano transcription approach using learned feature representations.
ISMIR 2011. [PDF]
J. Ngiam, Z. Chen, P. Koh, A.Y. Ng.
Learning deep energy models
ICML 2011. [PDF]
J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, A.Y. Ng.
Multimodal deep learning
ICML 2011. [PDF]
also appeared in the Deep Learning and Unsupervised Feature Learning Workshop (NIPS 2010). [PDF] Q.V. Le, J. Ngiam, A. Coates, A. Lahiri, B. Prochnow, A.Y. Ng.
On optimization methods for deep learning
ICML 2011. [PDF]
Q.V. Le, J. Ngiam, Z. Chen, D. Chia, P. Koh, A.Y. Ng.
Tiled convolutional neural networks.
NIPS 2010. [PDF] [Visualizations]

Code, Tutorials and Courses

Sparse Filtering
Matlab code that demonstrates how to run sparse filtering to train a two layer network.
http://github.com/jngiam/sparseFiltering
(Clone using

git clone --recursive git://github.com/jngiam/sparseFiltering.git

) Deep Learning Tutorial
Tutorial on deep learning, covering sparse autoencoders, whitening, softmax regrssion, deep neural networks, convolution and pooling.
http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial Machine Learning Class
Machine learning course with a focus on applications; course covers supervised learning (classification, regression), unsupervised learning (clustering, dimension reduction) and recommender systems.
http://www.ml-class.org/ Score Matching with Independent Component Analysis (ICA)
Matlab code that learns overcomplete ICA bases using Score Matching and minFunc. Very fast!
http://github.com/jngiam/scoreMatching
(Clone using

git clone --recursive git://github.com/jngiam/scoreMatching.git

)