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ICML
2009
IEEE
15 years 10 months ago
Using fast weights to improve persistent contrastive divergence
The most commonly used learning algorithm for restricted Boltzmann machines is contrastive divergence which starts a Markov chain at a data point and runs the chain for only a few...
Tijmen Tieleman, Geoffrey E. Hinton
CVPR
2006
IEEE
16 years 5 months ago
Improving Recognition of Novel Input with Similarity
Many sources of information relevant to computer vision and machine learning tasks are often underused. One example is the similarity between the elements from a novel source, suc...
Jerod J. Weinman, Erik G. Learned-Miller
UAI
1996
15 years 4 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
ICCV
2005
IEEE
16 years 5 months ago
A Maximum Entropy Framework for Part-Based Texture and Object Recognition
This paper presents a probabilistic part-based approach for texture and object recognition. Textures are represented using a part dictionary found by quantizing the appearance of ...
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
ATAL
2003
Springer
15 years 8 months ago
An evolutionary framework for studying behaviors of economic agents
We propose an evolutionary framework for studying agents that interact in electronic marketplaces. We describe how this framework could be used to study the dynamics of interactio...
Wolfgang Ketter, Alexander Babanov, Maria L. Gini