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» Variable selection using neural-network models
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ICML
2008
IEEE
15 years 10 months ago
On the quantitative analysis of deep belief networks
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Ruslan Salakhutdinov, Iain Murray
76
Voted
CORR
2007
Springer
164views Education» more  CORR 2007»
14 years 9 months ago
Consistency of the group Lasso and multiple kernel learning
We consider the least-square regression problem with regularization by a block 1-norm, that is, a sum of Euclidean norms over spaces of dimensions larger than one. This problem, r...
Francis Bach
NIPS
2008
14 years 11 months ago
Clusters and Coarse Partitions in LP Relaxations
We propose a new class of consistency constraints for Linear Programming (LP) relaxations for finding the most probable (MAP) configuration in graphical models. Usual cluster-base...
David Sontag, Amir Globerson, Tommi Jaakkola
CASES
2001
ACM
15 years 1 months ago
Storage allocation for embedded processors
In an embedded system, it is common to have several memory areas with different properties, such as access time and size. An access to a specific memory area is usually restricted...
Jan Sjödin, Carl von Platen
JCDL
2009
ACM
179views Education» more  JCDL 2009»
15 years 4 months ago
Disambiguating authors in academic publications using random forests
Users of digital libraries usually want to know the exact author or authors of an article. But different authors may share the same names, either as full names or as initials and...
Pucktada Treeratpituk, C. Lee Giles