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» Robust Learning - Rich and Poor
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ICML
2010
IEEE
15 years 2 months ago
Deep networks for robust visual recognition
Deep Belief Networks (DBNs) are hierarchical generative models which have been used successfully to model high dimensional visual data. However, they are not robust to common vari...
Yichuan Tang, Chris Eliasmith
135
Voted
SEAL
1998
Springer
15 years 6 months ago
Robust Evolution Strategies
This paper empirically investigates the use and behaviour of Evolution Strategies (ES) algorithms on problems such as function optimisation and the use of evolutionary artificial ...
Kazuhiro Ohkura, Yoshiyuki Matsumura, Kanji Ueda
139
Voted
CVPR
2012
IEEE
13 years 4 months ago
Robust Boltzmann Machines for recognition and denoising
While Boltzmann Machines have been successful at unsupervised learning and density modeling of images and speech data, they can be very sensitive to noise in the data. In this pap...
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hi...
108
Voted
CVPR
2003
IEEE
16 years 3 months ago
Bootstrapping SVM Active Learning by Incorporating Unlabelled Images for Image Retrieval
The performance of image retrieval with SVM active learning is known to be poor when started with few labelled images only. In this paper, the problem is solved by incorporating t...
Lei Wang, Kap Luk Chan, Zhihua Zhang
ATAL
2004
Springer
15 years 7 months ago
When to Apply the Fifth Commandment: The Effects of Parenting on Genetic and Learning Agents
This paper explores hybrid agents that use a variety of techniques to improve their performance in an environment over time. We considered, specifically, geneticlearning-parentin...
Michael Berger, Jeffrey S. Rosenschein