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JMLR
2012
11 years 11 months ago
Deep Learning Made Easier by Linear Transformations in Perceptrons
We transform the outputs of each hidden neuron in a multi-layer perceptron network to have zero output and zero slope on average, and use separate shortcut connections to model th...
Tapani Raiko, Harri Valpola, Yann LeCun
ICRA
2008
IEEE
150views Robotics» more  ICRA 2008»
14 years 3 months ago
A Bayesian approach to empirical local linearization for robotics
— Local linearizations are ubiquitous in the control of robotic systems. Analytical methods, if available, can be used to obtain the linearization, but in complex robotics system...
Jo-Anne Ting, Aaron D'Souza, Sethu Vijayakumar, St...
AAAI
2012
11 years 11 months ago
Supervised Probabilistic Robust Embedding with Sparse Noise
Many noise models do not faithfully reflect the noise processes introduced during data collection in many real-world applications. In particular, we argue that a type of noise re...
Yu Zhang, Dit-Yan Yeung, Eric P. Xing
PKDD
2010
Springer
184views Data Mining» more  PKDD 2010»
13 years 7 months ago
Shift-Invariant Grouped Multi-task Learning for Gaussian Processes
Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task ...
Yuyang Wang, Roni Khardon, Pavlos Protopapas
CVPR
2005
IEEE
14 years 11 months ago
Face Recognition with Image Sets Using Manifold Density Divergence
In many automatic face recognition applications, a set of a person's face images is available rather than a single image. In this paper, we describe a novel method for face r...
Ognjen Arandjelovic, Gregory Shakhnarovich, John F...