Tom Le Paine

Research Scientist at DeepMind.
Previously: 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
Google scholar
   

Work

DeepMind
Current

Google AI
Summer 2015

Google Brain
Summer 2014

MILA
Spring 2014

Adobe
Summer 2013

BNI
2008 - 2009


Publications

Large-Scale Visual Speech Recognition

Brendan Shillingford, Yannis Assael, Matthew W. Hoffman, Tom Le Paine, Cían Hughes, Utsav Prabhu, Hank Liao, Hasim Sak, Kanishka Rao, Lorrayne Bennett, Marie Mulville, Ben Coppin, Ben Laurie, Andrew Senior, Nando de Freitas

Pre-print
PDF

Playing Hard Exploration Games by Watching YouTube

Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, Nando de Freitas

Pre-print
PDF

Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions

Scott Reed, Yutian Chen, Tom Le Paine, Aäron van den Oord, S. M. Ali Eslami, Danilo Rezende, Oriol Vinyals, Nando de Freitas

ICLR 2018
PDF

Fast Generation for Convolutional Autoregressive Models

Tom Le Paine, Prajit Ramachandran, Pooya Khorrami, Mohammad Babaeizadeh, Shiyu Chang, Yang Zhang, Mark A. Hasegawa-Johnson, Roy H. Campbell, Thomas S. Huang

ICLR 2017 (Workshop)
PDF

How Deep Neural Networks Can Improve Emotion Recognition on Video Data

Pooya Khorrami, Tom Le Paine, name, name, Thomas S. Huang

ICIP 2016
PDF

Seq-NMS for Video Object Detection

Tom Le Paine, Wei Han, Pooya Khorrami, Prajit Ramachandran, Mohammad Babaeizadeh, Honghui Shi, Jianan Li, Shuicheng Yan, Thomas S. Huang

ICCV 2015 (Workshop)
PDF

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