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ICML
2008
IEEE
16 years 16 days 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
83
Voted
CDC
2009
IEEE
135views Control Systems» more  CDC 2009»
15 years 4 months ago
Trust Estimation in autonomic networks: a statistical mechanics approach
— Trust management, broadly intended as the ability to maintain belief relationship among entities, is recognized as a fundamental security challenge for autonomous and selforgan...
Stefano Ermon, Luca Schenato, Sandro Zampieri
UAI
1997
15 years 1 months ago
Update Rules for Parameter Estimation in Bayesian Networks
This paper re-examines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in on-line learning [1...
Eric Bauer, Daphne Koller, Yoram Singer
CORR
2007
Springer
135views Education» more  CORR 2007»
14 years 11 months ago
Detailed Network Measurements Using Sparse Graph Counters: The Theory
— Measuring network flow sizes is important for tasks like accounting/billing, network forensics and security. Per-flow accounting is considered hard because it requires that m...
Yi Lu, Andrea Montanari, Balaji Prabhakar
SDM
2010
SIAM
166views Data Mining» more  SDM 2010»
14 years 10 months ago
Directed Network Community Detection: A Popularity and Productivity Link Model
In this paper, we consider the problem of community detection in directed networks by using probabilistic models. Most existing probabilistic models for community detection are ei...
Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, ...