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134
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ICML
2008
IEEE
16 years 3 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
113
Voted
JMLR
2010
103views more  JMLR 2010»
14 years 9 months ago
Learning Nonlinear Dynamic Models from Non-sequenced Data
Virtually all methods of learning dynamic systems from data start from the same basic assumption: the learning algorithm will be given a sequence of data generated from the dynami...
Tzu-Kuo Huang, Le Song, Jeff Schneider
FOCS
2008
IEEE
15 years 9 months ago
Hardness of Minimizing and Learning DNF Expressions
We study the problem of finding the minimum size DNF formula for a function f : {0, 1}d → {0, 1} given its truth table. We show that unless NP ⊆ DTIME(npoly(log n) ), there i...
Subhash Khot, Rishi Saket
155
Voted
APCHI
2008
IEEE
15 years 9 months ago
On Generating Backbone Trees through Robust Multi-hop Clusters in Wireless Sensor Networks
Routing through a backbone, which is responsible for performing and managing multipoint communication, reduces the communication overhead and overall energy consumption in wireless...
Inyoung Shin, Moonseong Kim, Hyunseung Choo
169
Voted
TSMC
2010
14 years 9 months ago
Distance Approximating Dimension Reduction of Riemannian Manifolds
We study the problem of projecting high-dimensional tensor data on an unspecified Riemannian manifold onto some lower dimensional subspace1 without much distorting the pairwise geo...
Changyou Chen, Junping Zhang, Rudolf Fleischer