Tom Le Paine

Applying deep learning to computer vision: speeding it up, and making it work with less labeled data. PhD student in the Image Formation and Processing group at UIUC, advised by Professor Thomas Huang.

tom.le.paine@gmail.com / paine1@illinois.edu
Google scholar
   

Work

UIUC
Current

Google
Summer 2015

Google
Summer 2014

MILA
Spring 2014

Adobe
Summer 2013

BNI
2008 - 2009


Publications

Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?

Pooya Khorrami, Tom Le Paine, Thomas S. Huang

ICCV 2015 (Workshop)
PDF

An Analysis of Unsupervised Pre-training in light of recent advances

Tom Le Paine, Pooya Khorrami, Wei Han, Thomas S. Huang

ICLR 2015 (Workshop)
PDF Code (Github)

Visual Media: History and Perspectives

Thomas S. Huang, Vuong Le, Tom Le Paine, Pooya Khorrami, Usman Tariq

IEEE Multimedia 2014
PDF

GPU asynchronous stochastic gradient descent to speed up neural network training

Tom Le Paine, Hailin Jin, Jianchao Yang, Zhe Lin, Thomas S. Huang

ICLR 2014 (Workshop)
PDF

Deep learning for face recognition

Tom Le Paine, Thapanapong Rukkanchanunt

Technical report
PDF

Simultaneous dynamic and functional MRI scanning (SimulScan) of natural swallows

Tom Le Paine, Charles Conway, Georgia Malandraki, Bradley Sutton

Magnetic Resonance in Medicine
PDF

Examination of susceptibility effects on functional and dynamic magnetic resonance imaging

Tom Le Paine

UIUC Master's thesis
PDF

Optimized preload leakage-correction methods to improve the diagnostic accuracy of dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in posttreatment gliomas

LS Hu, LC Baxter, DSPinnaduwage, TL Paine, JP Karis, BG Feuerstein, KM Schmainda, AC Dueck, J Debbins, KA Smith, P Nakaji, JM Eschbacher, SW Coons, JE Heiserman

American Journal of Neuroradiology
PDF

Code

Anna

A python micro-framework for training neural networks. Built on top of Theano. Written with the help of my colleague Pooya Khorrami.

from anna import layers
input = layers.Input()
conv1 = layers.Conv2DLayer(
	input=input,
	n_features=200)
Github

Teaching

Spring 2013
Teaching assistant for CS 543: Computer Vision