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» Robust feature extraction via information theoretic learning
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ICML
2009
IEEE
14 years 9 months ago
Robust feature extraction via information theoretic learning
In this paper, we present a robust feature extraction framework based on informationtheoretic learning. Its formulated objective aims at simultaneously maximizing the Renyi's...
Xiaotong Yuan, Bao-Gang Hu
ICML
2008
IEEE
14 years 9 months ago
Extracting and composing robust features with denoising autoencoders
Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to use...
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pi...
IADIS
2004
13 years 10 months ago
Towards a Theoretical Framework for Informal Language Learning Via Interactive Television
This paper proposes a pedagogical framework for informal language learning services via interactive television. We argue that mapping current language learning theories onto learn...
Lyn Pemberton, Sanaz Fallahkhair, Judith Masthoff
ICML
2010
IEEE
13 years 9 months ago
A Theoretical Analysis of Feature Pooling in Visual Recognition
Many modern visual recognition algorithms incorporate a step of spatial `pooling', where the outputs of several nearby feature detectors are combined into a local or global `...
Y-Lan Boureau, Jean Ponce, Yann LeCun
SIGIR
2003
ACM
14 years 1 months ago
Text categorization by boosting automatically extracted concepts
Term-based representations of documents have found widespread use in information retrieval. However, one of the main shortcomings of such methods is that they largely disregard le...
Lijuan Cai, Thomas Hofmann