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129
Voted
ICML
2010
IEEE
15 years 2 months ago
2010
IEEE
Existing approaches to analyzing the asymptotics of graph Laplacians typically assume a well-behaved kernel function with smoothness assumptions. We remove the smoothness assumpti...
107
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ICML
2010
IEEE
15 years 2 months ago
2010
IEEE
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...
111
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ICML
2010
IEEE
15 years 2 months ago
2010
IEEE
The structure of a Markov network is typically learned using top-down search. At each step, the search specializes a feature by conjoining it to the variable or feature that most ...
107
click to vote
ICML
2010
IEEE
15 years 2 months ago
2010
IEEE
Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and disc...
108
Voted
ICML
2010
IEEE
15 years 2 months ago
2010
IEEE
We show that the mistake bound for predicting the nodes of an arbitrary weighted graph is characterized (up to logarithmic factors) by the cutsize of a random spanning tree of the...